Gene_expression

Gene expression - laboratory infections

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Heatmap on gene expression data:

Gene correlation

Correlations between genes:

corrplot(gene_correlation, 
         method = "circle",  #method of the plot, "color" would show colour gradient
         tl.col = "black", tl.srt=45, #colour of labels and rotation
         col = brewer.pal(n = 8, name ="RdYlBu"), #colour of matrix
         order="hclust") #hclust reordering

Correlations between genes, only including significant values

### Adding correlation coefficients

gene_expr %>%
    group_by(EH_ID) %>%
  pivot_longer(cols = 8:26, names_to = "Gene", values_to = "gene_expression") %>%
  ggplot(aes(x = gene_expression, color = challenge_infection)) + 
  geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 159 rows containing non-finite values (stat_bin).

gene_expr %>%
    group_by(EH_ID) %>%
  filter(!challenge_infection == "UNI") %>%
  pivot_longer(cols = 8:26, names_to = "Gene", values_to = "gene_expression") %>%
  ggplot(aes(x = gene_expression, color = challenge_infection)) + 
  geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 90 rows containing non-finite values (stat_bin).

summary(gene_expr)

##     EH_ID           primary_infection  challenge_infection infection_history 
##  Length:116         Length:116         Length:116          Length:116        
##  Class :character   Class :character   Class :character    Class :character  
##  Mode  :character   Mode  :character   Mode  :character    Mode  :character  
##                                                                              
##                                                                              
##                                                                              
##                                                                              
##  mouse_strain           max_WL           delta           CXCR3_bio    
##  Length:116         Min.   : 73.45   Min.   :-12.690   Min.   :16.86  
##  Class :character   1st Qu.: 89.14   1st Qu.: -8.600   1st Qu.:20.08  
##  Mode  :character   Median : 94.14   Median : -6.065   Median :21.15  
##                     Mean   : 92.37   Mean   : -5.065   Mean   :21.25  
##                     3rd Qu.: 97.28   3rd Qu.: -3.741   3rd Qu.:22.57  
##                     Max.   :100.00   Max.   : 11.610   Max.   :25.80  
##                                      NA's   :6                        
##       IL.6           IL.10           IL.13           IL1RN      
##  Min.   :13.22   Min.   :17.79   Min.   :14.24   Min.   :10.39  
##  1st Qu.:19.52   1st Qu.:21.48   1st Qu.:16.11   1st Qu.:14.71  
##  Median :22.03   Median :23.22   Median :18.64   Median :16.91  
##  Mean   :22.21   Mean   :23.24   Mean   :18.60   Mean   :16.37  
##  3rd Qu.:24.97   3rd Qu.:24.85   3rd Qu.:20.56   3rd Qu.:18.21  
##  Max.   :29.95   Max.   :29.99   Max.   :24.66   Max.   :23.14  
##  NA's   :10      NA's   :10      NA's   :86                     
##      CASP1           CXCL9            IDO1            IRGM1       
##  Min.   :19.96   Min.   :10.83   Min.   : 8.662   Min.   : 7.028  
##  1st Qu.:21.28   1st Qu.:14.62   1st Qu.:12.506   1st Qu.: 8.838  
##  Median :22.62   Median :17.65   Median :15.631   Median : 9.472  
##  Mean   :22.85   Mean   :17.74   Mean   :15.815   Mean   : 9.579  
##  3rd Qu.:23.81   3rd Qu.:20.04   3rd Qu.:18.434   3rd Qu.:10.301  
##  Max.   :29.99   Max.   :25.74   Max.   :27.170   Max.   :14.225  
##  NA's   :2                                                        
##       MPO             MUC2            MUC5AC           MYD88      
##  Min.   :15.61   Min.   : 6.211   Min.   : 7.526   Min.   : 8.79  
##  1st Qu.:17.66   1st Qu.: 7.942   1st Qu.: 9.053   1st Qu.:11.32  
##  Median :21.50   Median : 8.570   Median :10.101   Median :16.16  
##  Mean   :21.82   Mean   : 8.786   Mean   :11.598   Mean   :16.17  
##  3rd Qu.:25.42   3rd Qu.: 9.357   3rd Qu.:12.309   3rd Qu.:19.00  
##  Max.   :29.21   Max.   :17.991   Max.   :29.918   Max.   :28.08  
##  NA's   :15                                                       
##       NCR1            PRF1           RETNLB           SOCS1       
##  Min.   :17.17   Min.   :18.08   Min.   : 3.437   Min.   : 7.087  
##  1st Qu.:21.69   1st Qu.:23.49   1st Qu.: 8.444   1st Qu.: 9.675  
##  Median :23.71   Median :25.39   Median : 9.915   Median :10.466  
##  Mean   :23.50   Mean   :25.08   Mean   :10.247   Mean   :10.606  
##  3rd Qu.:25.43   3rd Qu.:27.10   3rd Qu.:11.533   3rd Qu.:11.518  
##  Max.   :29.55   Max.   :29.79   Max.   :22.021   Max.   :15.561  
##  NA's   :10      NA's   :23                                       
##      TICAM1           TNF        Parasite_challenge
##  Min.   :12.41   Min.   :13.79   Length:116        
##  1st Qu.:16.51   1st Qu.:19.37   Class :character  
##  Median :19.12   Median :21.17   Mode  :character  
##  Mean   :19.13   Mean   :21.43                     
##  3rd Qu.:21.48   3rd Qu.:23.07                     
##  Max.   :29.58   Max.   :29.87                     
##  NA's   :1       NA's   :2

g <- gene_expr

It is possible to compute a pca with missing data using the package missMDA. The missMDA package is dedicated to missing values in exploratory multivariate data analysis: single imputation/multiple imputation, etc.

Following the tutorial of the package author: Francois Husson: https://www.youtube.com/watch?v=OOM8_FH6_8o

Handling missing data in a pca:

Bad methods: removing individuals with missing data or replacing missing data with the mean (default setting in many packages).

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## Warning in PCA(g[8:26]): Missing values are imputed by the mean of the variable:
## you should use the imputePCA function of the missMDA package

#let's do a pca while removing ALL NA values
res.NA.remove <- PCA(g[8:26] %>% na.omit())

# These are rather unsophisticated ways to solve the problem

We will now continue by using an iterative pca to impute missing data A. Initialization: impute using the mean B. Step lampda: # a. do pca on imputed data table S dimensions retained # b. missing data imputed using pca # c. means (and standard deviations) updated C. Iterate the estimation and imputation steps (until convergence) (convergence: the act of converging and especially moving toward union or uniformity)

Overfitting is a common problem due to believing too much in links between variables. –> regularized iterative PCA (This version is what is being implented in missMDA) This is a way of taking less risk when imputing the missing data. The algorithm estimates the missing data values with values that have no influence on the PCA results, i.e., no influence on the coordinates of the individals or variables.

## $Dim.1
## $quanti
##           correlation      p.value
## IL.13       0.9776601 4.575076e-79
## TNF         0.9206774 2.139356e-48
## IDO1        0.8649672 6.274569e-36
## IL.10       0.8184320 3.355024e-29
## RETNLB      0.8145332 1.001197e-28
## CXCL9       0.7465281 6.676802e-22
## PRF1        0.7402907 2.194441e-21
## TICAM1      0.7333679 7.903732e-21
## NCR1        0.6839699 2.623696e-17
## CXCR3_bio   0.6787540 5.630664e-17
## MYD88       0.6558009 1.356908e-15
## IL.6        0.6172165 1.600776e-13
## IL1RN       0.5396153 4.094022e-10
## MPO         0.3017989 9.933861e-04
## MUC5AC      0.2260973 1.466854e-02
## CASP1       0.2136150 2.131084e-02
## SOCS1      -0.3180971 5.018860e-04
## 
## attr(,"class")
## [1] "condes" "list"  
## 
## $Dim.2
## $quanti
##        correlation      p.value
## IRGM1    0.7763684 1.339074e-24
## SOCS1    0.6913736 8.632991e-18
## NCR1     0.3780309 2.867164e-05
## TICAM1   0.3744576 3.457090e-05
## MUC2     0.2213188 1.696110e-02
## PRF1     0.2195441 1.788836e-02
## CXCL9    0.2068282 2.590529e-02
## CASP1   -0.3680234 4.815534e-05
## MUC5AC  -0.4935210 1.812712e-08
## IL1RN   -0.5970273 1.511949e-12
## MPO     -0.8303219 1.010067e-30
## 
## attr(,"class")
## [1] "condes" "list"  
## 
## $Dim.3
## $quanti
##           correlation      p.value
## MUC2        0.8287201 1.644715e-30
## CASP1       0.7881238 8.851803e-26
## MUC5AC      0.6523399 2.141080e-15
## SOCS1       0.5260068 1.330377e-09
## IRGM1       0.4484578 4.452910e-07
## CXCR3_bio   0.2532317 6.093521e-03
## MYD88       0.2141508 2.097999e-02
## CXCL9      -0.1993732 3.190083e-02
## TICAM1     -0.2244631 1.542027e-02
## 
## attr(,"class")
## [1] "condes" "list"  
## 
## $call
## $call$num.var
## [1] 1
## 
## $call$proba
## [1] 0.05
## 
## $call$weights
##   [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1
## 
## $call$X
##            Dim.1 CXCR3_bio     IL.6    IL.10    IL.13    IL1RN    CASP1
## 1   -0.915545774  20.92666 21.09045 21.78837 18.20471 16.42338 22.02920
## 2    0.011780083  21.62075 25.32600 22.92255 19.01956 20.13510 24.25054
## 3    1.584363191  23.66537 24.18021 24.90025 20.31376 18.14916 22.55511
## 4   -1.818030087  20.21312 23.90781 22.31029 17.23688 16.79377 27.50341
## 5    2.034804392  23.02829 23.19571 27.67319 20.87536 18.98532 25.45624
## 6    2.832029262  23.18574 22.59572 25.82543 21.46693 19.45825 23.14097
## 7   -0.570846800  20.19632 23.91450 21.62915 18.46942 18.55582 23.11127
## 8    0.809533133  23.73105 21.14346 23.61026 19.35098 17.67666 25.06357
## 9    1.765621773  23.18462 22.02135 24.86233 20.33561 19.31653 22.45011
## 10  -3.291836424  19.21698 19.62519 22.94861 15.86227 17.29027 23.55407
## 11   2.011495300  22.52077 23.46206 23.85751 20.85859 20.58918 23.81598
## 12  -3.125533593  19.12177 25.26331 20.62422 16.15499 17.38254 22.70095
## 13   0.054794456  22.45032 22.69335 23.62850 18.93492 17.74972 22.49518
## 14  -0.372270923  22.51152 20.95665 23.01596 18.52967 16.69397 22.84267
## 15   0.775923951  21.26747 21.85538 23.82383 19.73429 19.67336 22.04784
## 16  -3.512223468  18.00746 18.38914 18.74826 15.90985 15.10803 23.38067
## 17  -2.001392829  19.10121 20.98553 22.42743 17.18706 16.44270 23.42149
## 18   1.660922890  22.41295 20.25383 23.03015 20.32417 17.63167 22.85872
## 19  -0.966654087  21.44775 18.45020 27.89777 17.76739 17.32784 23.05298
## 20  -1.864788567  21.07130 20.03526 21.08242 17.11006 17.43922 22.51961
## 21  -1.007482453  20.92691 21.09167 24.07971 17.98829 17.14403 22.76425
## 22  -3.584868773  19.36417 15.07182 20.66591 15.58336 16.54046 24.16598
## 23  -3.678212445  18.14328 17.39709 21.31080 15.58911 12.63218 23.57993
## 24  -0.841502706  20.03795 28.89199 21.07542 18.86096 18.66006 29.94250
## 25   1.500264009  23.44335 23.78059 24.23830 20.61809 16.75777 23.76993
## 26  -1.926479521  20.31368 19.93494 21.62254 17.14027 17.64324 22.86814
## 27   3.113068945  24.81556 21.68421 25.12770 21.75416 18.20610 22.86023
## 28   0.092018252  20.53629 25.89126 23.68561 19.46835 16.66748 28.45142
## 29  -0.742775277  20.53802 20.02823 20.76649 18.13779 17.42489 22.69451
## 30  -4.195983201  18.97607 19.07150 21.15728 14.99924 13.40232 24.16373
## 31   0.607274587  23.27692 19.71861 23.52992 19.56396 19.58139 21.79801
## 32  -0.002842419  21.66393 20.33407 20.44784 19.29409 17.72250 23.41526
## 33   0.778082720  23.07634 22.39279 23.94323 19.60995 19.91583 23.28426
## 34  -0.520873430  22.53733 18.60527 22.20044 18.41209 17.97018 25.19149
## 35   0.799691732  22.10287 20.06929 24.09205 19.58568 19.46909 21.63034
## 36   1.575786400  24.60278 18.46774 26.19512 20.22045 19.76563 23.74273
## 37  -3.860247890  18.58505 19.05836 17.86940 15.74421 14.97143 20.93711
## 38  -6.674900348  16.86408 13.21835 17.79117 12.78276 14.25306 24.43256
## 39  -0.809016843  22.15447 25.41724 22.04275 18.16404 17.18358 29.98603
## 40  -0.853391090  21.16552 18.10097 22.65766 18.21368 17.71976 24.28977
## 41   0.367541349  24.69742 19.05980 23.16757 19.17256 18.98606 22.78772
## 42   0.728066595  21.32868 21.65526 24.18590 19.50128 17.83004 22.53197
## 43  -5.040384134  18.70600 17.79219 18.49236 14.33358 14.68719 24.20202
## 44   0.848172054  22.12217 21.87536 23.32834 19.85906 18.26465 23.79661
## 45  -4.500887228  19.16785 17.97426 19.89393 14.81195 15.60916 23.59150
## 46  -3.819212366  18.19756 14.40759 20.83681 15.46550 17.24873 25.16027
## 47  -2.043532059  21.64540 17.73381 20.98086 16.94110 17.40872 20.43007
## 48  -3.090244335  20.18662 16.57967 19.05747 16.20012 16.32045 22.82450
## 49  -3.974399445  18.21390 29.81903 18.33959 15.70261 13.28305 25.93133
## 50   0.868149879  20.61515 23.03621 29.98877 19.87497 12.75221 20.43024
## 51   1.528209216  21.00294 23.67766 24.20689 20.54626 15.08824 20.90917
## 52  -1.484400934  20.65725 21.54047 23.25961 17.66527 11.65156 22.92319
## 53   1.895868415  21.31698 26.71171 25.52867 20.96153 13.83283 21.43852
## 54  -0.847813440  20.20715 24.37765 22.34004 18.42687 11.97324 21.03117
## 55  -3.501222446  18.40655 21.07280 21.44767 15.95547 11.82725 21.70017
## 56  -0.561376714  20.89049 23.48067 22.36207 18.55236 11.85427 21.44578
## 57   0.066074387  21.14188 24.07659 23.60720 19.22818 13.10425 25.34167
## 58   1.517001465  21.95768 22.34438 26.11948 20.51676 15.37061 21.28205
## 59  -2.701493670  18.21933 25.24031 20.51353 16.79688 12.28923 22.26009
## 60   0.399702758  24.65157 23.53264 23.43752 19.37199 11.43627 25.73564
## 61   0.421502299  23.52242 27.13311 24.12098 19.47855 11.27634 21.92730
## 62   5.393204085  22.91576 25.39795 28.87344 23.88989 16.75650 22.16068
## 63   5.889680025  25.66519 26.11602 27.58330 24.25735 19.70521 26.08998
## 64   7.098575665  24.22064 29.76292 28.05381 25.62352 19.23186 27.75083
## 65   3.982560186  22.86653 23.56129 25.01750 22.69070 18.03112 21.69953
## 66   4.281134917  22.76071 26.78894 27.95595 23.15535 15.84708 23.58463
## 67   6.831011961  23.52785 29.95433 28.18004 25.45059 23.13962 27.11709
## 68   5.540614286  22.75096 27.96307 25.69999 24.20575 17.58811 23.01611
## 69   3.611356503  21.48844 22.60560 29.13131 22.36904 18.63039 20.75492
## 70   7.596823857  25.80037 29.77186 29.09962 26.17544 20.02498 26.91510
## 71   2.874537418  21.03955 26.23716 26.44454 21.73347 16.61041 22.30920
## 72   4.795566060  21.52073 27.46334 24.91564 23.29033 17.10527 24.02626
## 73   4.537132227  22.66652 18.92939 25.56209 22.76709 18.86001 24.83386
## 74  -0.593108710  21.81679 20.56615 26.30993 18.24177 12.25711 21.65407
## 75   6.716558268  22.67323 26.18491 27.22396 25.27947 20.08638 25.34344
## 76   3.517995949  23.46890 22.68031 23.16663 22.11858 18.06204 22.36512
## 77  -1.779229385  19.91255 22.64689 22.45926 17.54585 11.27495 21.54675
## 78  -2.054941315  20.08603 21.31040 22.15091 17.23761 10.39335 20.70334
## 79   4.392302673  24.06576 28.75247 23.54490 23.10769 21.42000 27.75544
## 80  -1.386659442  21.27995 18.29274 24.38039 17.57979 12.06338 20.29093
## 81  -2.636669055  20.22281 24.09667 19.95704 16.75355 12.45559 21.84692
## 82   1.498389308  24.54876 23.94933 25.98836 20.62077 14.72332 24.56166
## 83   1.486028669  19.66524 28.21305 25.28405 20.68618 12.24016 21.28489
## 84   1.002576368  21.37939 22.23118 24.59390 20.11525 17.24273 21.67512
## 85  -1.609344620  20.76001 22.04766 22.25595 17.53079 15.60735 20.92126
## 86  -1.014946561  18.55728 25.20569 24.22079 18.42338 15.59951 19.99790
## 87   3.064475490  20.76581 23.12287 24.20175 24.65534 16.35892 21.11699
## 88  -0.287117840  19.80003 24.82849 24.68163 18.29073 12.63565 23.09224
## 89  -0.125845513  20.95861 24.47956 26.12950 20.04791 11.69210 22.80543
## 90  -0.409897637  21.59717 26.58406 21.70019 21.35713 11.07716 20.93479
## 91  -2.832695773  18.34869 21.28534 21.67449 15.43664 14.73684 19.96002
## 92  -0.496023933  21.69704 25.01886 23.22688 17.92247 15.92684 21.19813
## 93  -0.437785486  21.45424 19.31163 23.59355 18.98706 18.17187 22.41502
## 94  -5.684911320  19.13856 16.59937 17.90145 14.23954 12.16899 21.02919
## 95   0.192036635  20.68170 26.84019 24.26723 19.69519 17.50911 20.48537
## 96   1.043987030  18.54700 25.63743 23.37539 20.64206 17.68361 21.37431
## 97  -0.913418070  20.46746 18.96176 24.76140 17.74507 17.02771 20.42448
## 98  -0.008433619  19.20522 21.60006 22.75786 19.52374 19.51251 20.75696
## 99  -1.770740100  21.53098 18.24249 22.16032 17.34487 13.69251 24.61451
## 100  1.668914450  20.54109 26.92355 21.56791 22.61048 20.05260 24.82729
## 101 -1.991726509  20.32615 23.60529 21.83610 16.52889 15.60828 20.62177
## 102 -2.078620987  20.58743 22.64934 21.05282 15.64012 15.78011 21.04427
## 103 -2.075698591  21.30977 24.81393 20.53981 15.96387 15.62994 22.11439
## 104  0.017283801  24.78347 20.20337 23.52439 19.78487 14.97696 22.93029
## 105  2.711804234  22.77867 25.05195 25.24735 23.10120 20.21300 25.57550
## 106 -3.650027373  20.05828 17.73281 19.67574 14.96183 14.91150 20.39827
## 107 -2.917951946  20.06144 19.48198 22.33842 15.84020 15.91221 20.98834
## 108 -2.940622156  21.33039 19.22344 21.16372 16.83008 12.50428 22.24713
## 109 -2.725183539  21.08006 18.49392 19.74151 15.73607 12.83904 21.46682
## 110  1.495490699  18.95654 21.82732 26.77299 21.41995 19.69194 20.78469
## 111  1.879367204  20.24860 28.76398 23.93211 21.10862 20.95269 21.23007
## 112  0.873089477  19.01495 26.92797 23.32124 20.67509 18.21358 20.67898
## 113 -0.884056174  22.14929 22.09470 24.41267 16.81424 17.08754 20.84055
## 114 -4.035682818  23.41073 17.46569 19.79551 14.49658 13.13472 25.08039
## 115  0.791002902  18.84797 25.09494 24.87735 20.31023 15.62935 20.16156
## 116  0.612760349  21.05519 27.11805 23.21401 20.25871 18.40590 21.07134
##        CXCL9      IDO1     IRGM1      MPO      MUC2    MUC5AC     MYD88
## 1   13.60226 13.685507 11.625516 23.16109 11.394231 12.368312 16.856985
## 2   14.53048 12.347823 10.033986 26.67972  9.724516 14.599135 18.010443
## 3   18.99093 15.902410  7.810604 25.81143  7.749293 12.871210 20.059938
## 4   14.03929 12.783337 10.157602 27.67628  7.183272 14.041496 15.618948
## 5   19.20542 18.254268  9.241544 26.30798  9.869590 14.371520 17.538455
## 6   19.07817 18.488880  9.197374 24.94612  8.225922 11.583533 20.053889
## 7   14.67773 14.430931  8.600942 24.90775  8.730690 11.900492 18.177256
## 8   14.21946 15.666291  8.297135 25.61896  7.522414 13.148207 19.038180
## 9   16.20309 14.952342  8.997360 29.21133  8.156661  8.684992 20.392755
## 10  12.88829 11.663551  9.052160 27.46451  8.642571 10.342714 14.618691
## 11  20.30617 16.930006  8.162201 25.54124  8.859693 15.460500 19.281729
## 12  13.01806 10.705361  7.565302 24.91439  6.904949 15.359870 14.612337
## 13  17.39209 15.675249  9.841508 25.19862  7.871219  8.678551 16.285136
## 14  15.54217 13.079090 10.548003 23.12428  9.808142 10.449504 16.981842
## 15  19.29231 18.558979  9.218357 28.14862  8.669347 10.198480 16.960683
## 16  17.12064 15.731242  9.193427 23.38627  8.394537 10.196126 14.609839
## 17  13.68531 12.914861  9.322633 21.87048  8.714876 12.295662 15.888646
## 18  18.63569 19.069602  9.447187 27.92150  8.040773  9.121950 17.378285
## 19  12.95849 14.508282  9.598510 27.07087  7.807939 10.415893 15.854892
## 20  14.61636 13.138920  8.335187 24.66545  7.790361  9.038129 16.616529
## 21  16.18053 13.646650  9.565223 25.42206  8.771323  9.468288 15.065539
## 22  15.03703 16.004009  8.438642 27.97673  8.473955 10.951688 12.671592
## 23  14.00879 11.186614  9.439790 24.48003  8.613752 20.293679 13.916375
## 24  14.61884 12.098614  8.954314 24.31267 17.990707 24.237810 15.120134
## 25  21.33524 18.017771 11.480787 19.99031 10.255215 10.923709 17.310957
## 26  16.94201 12.246575  8.748695 26.20443  7.940369 12.292991 15.518893
## 27  18.99404 18.253549 10.577026 24.31717  8.761090  9.023115 19.547397
## 28  16.56531 16.273956 13.691213 20.56654 12.038068 20.929919 15.678849
## 29  15.85200 12.968113  8.203141 26.76613  8.233775 17.425917 17.354687
## 30  13.93241 11.229936  9.725386 24.64733  6.814177 11.003653 13.408224
## 31  18.71677 17.629490 10.881357 25.72409 10.267396 11.219287 15.989496
## 32  19.96504 19.114217 11.739965 21.94526 12.198908 12.960735 16.747558
## 33  18.19233 16.734890  8.942380 24.38990  8.418066  9.847442 16.538393
## 34  18.54367 17.279974  9.036738 22.05586  7.435172 13.026381 16.057834
## 35  17.56039 18.416046  8.848435 25.72081  8.204233  9.882749 18.079438
## 36  18.40169 19.753084  9.752966 28.66910  9.577180 10.404196 17.139011
## 37  16.51682 16.471883 11.495214 20.75777 10.413618 11.989536 14.774482
## 38  13.82651  8.661838  7.154126 23.86028  7.951477 15.116064 10.230339
## 39  13.24525 13.915862  8.987010 27.49619  9.597302 21.741745 16.270490
## 40  17.31654 17.871126 10.481867 23.27109  9.326657  9.908069 15.881726
## 41  18.82635 17.110750 10.481346 24.32100  8.573053  9.253118 16.642453
## 42  17.26648 15.929076  8.958850 24.41816  7.632720  8.440455 19.807910
## 43  12.21244  9.754557  9.836893 23.37686  9.851718 11.585622 14.407068
## 44  20.24753 19.708596  9.977461 24.92838  8.415812  8.076470 17.337172
## 45  13.56512  9.842353  9.029007 24.40325  9.316026  9.889951 13.850419
## 46  16.38290 15.927925  8.049613 26.94072  7.563250 12.350998 11.128010
## 47  16.55790 15.251946  9.385581 27.61566  8.563067  9.972695 15.725963
## 48  17.97308 18.265865  9.486106 23.34994  7.878306 10.787435 12.493822
## 49  11.98283 10.025161  8.136754 27.16886 10.834516 29.918079 13.929742
## 50  18.32872 16.717158 10.030781 16.37685  8.541946  8.852514 20.404963
## 51  21.96734 18.171699  9.531294 15.92918  7.957801  8.211709 24.785884
## 52  16.90758 12.292333 11.168791 17.07884  8.345124 10.313463 15.319679
## 53  22.16466 18.373123  9.563630 16.38449  8.132526  8.572920 23.240718
## 54  16.66383 11.993194 11.576390 17.15236 10.280913 10.532018 18.139879
## 55  15.42738 12.523361  9.928879 16.55492  8.291121  9.120236 13.839477
## 56  16.72246 13.721075 10.479662 16.32184  8.641474  8.817069 19.929199
## 57  18.64811 15.843818 10.788702 17.07038  9.428260  9.364003 18.078884
## 58  22.49707 18.930756  8.050492 16.71614  7.611355  7.888725 20.995390
## 59  15.25164 11.803676 10.108555 16.57170  9.364101  9.848285 15.544608
## 60  20.65578 12.237259 11.398526 16.60661  9.628627  9.639826 19.190942
## 61  18.51135 13.708155 10.256888 16.39160  9.063478  9.058345 20.478204
## 62  23.45426 21.459525  7.149357 25.63594  6.211322 10.154484 24.906656
## 63  19.96006 20.724537  9.016223 26.44495  9.246984 14.123916 24.948713
## 64  23.07473 27.169505  8.986193 27.97227  8.883982 23.694956 27.782637
## 65  24.18800 22.517576  8.747040 26.39468  7.865111  9.540464 19.913584
## 66  23.33492 22.130637  9.505613 20.34651  9.117813 10.210623 25.644537
## 67  22.51919 24.362430  7.028294 31.62038  9.249620 25.681823 23.705403
## 68  24.12845 22.364820  7.679259 28.01318  7.529806 12.495365 24.056632
## 69  22.14808 21.229097  8.823074 24.08935  7.071763  8.979468 18.841489
## 70  23.73669 26.746953 10.566932 23.76439 10.301982 15.246147 24.071985
## 71  20.71644 20.531902  8.015308 26.53156  6.875894 15.616582 18.824360
## 72  18.86451 21.448918  7.324264 27.47612  6.626930 13.267206 25.219254
## 73  16.34429 22.097978  7.796770 29.42759  8.052046 18.066238 24.484515
## 74  13.14677 13.300336 10.259382 16.46249  8.504597  8.874519 20.875416
## 75  23.74179 25.029717  9.255368 27.99719  8.579815 29.113148 28.078962
## 76  19.88270 20.563533 10.008556 23.69222  8.371019  9.336553 24.674035
## 77  18.09229 12.713460 10.729052 16.12279  9.020236  9.326122 17.670409
## 78  18.47461 11.083212  9.706705 15.60862  8.225850  8.669181 17.396433
## 79  18.54329 18.841289  7.212158 28.72646 11.580169 26.744894 20.536848
## 80  14.49042 12.360175 10.292874 16.43850  8.630854  8.630570 17.291781
## 81  14.45594 11.304476  9.925298 17.66268  9.721816 10.491249 15.854700
## 82  22.72566 15.063209 14.224817 20.66631 12.365167 12.636119 18.984608
## 83  23.29208 13.730573 11.186153 16.54841  9.411632  9.773155 28.008621
## 84  19.01887 18.925460 10.323671 17.42215  8.655927  9.526401 18.953311
## 85  14.94362 12.729950  9.898791 17.78050  8.541098  8.728032 16.724469
## 86  18.02023 16.255302 10.261519 17.17013  8.567059  8.840712 17.923550
## 87  21.25767 21.997595  7.290380 28.70681  6.445662  9.138264 13.951069
## 88  20.79058 13.061516 11.602668 17.49598  9.719942 10.305993 10.797975
## 89  21.78558 12.274416 11.183381 17.01788  9.354890  9.778690 10.666151
## 90  22.47699 11.437480 10.668408 16.28529  8.850896  9.096841 10.200785
## 91  15.04185 13.407077  9.601861 16.79333  7.966876  8.117302  9.107979
## 92  18.33013 15.595453  9.425018 16.97132  7.794905  8.401166  9.336994
## 93  14.38687 12.455074  9.436140 24.79474  8.197041  9.244237  9.956077
## 94  10.82695  9.136530  9.375088 17.69353  8.866534  9.777502 10.271472
## 95  18.70781 14.946433  8.839694 19.82217  7.487277  8.125193  8.844189
## 96  24.86841 19.502043  9.457993 18.28340  8.071410  8.206934  9.252441
## 97  15.35375 14.252057  9.304423 23.43023  7.997043  9.088218  9.269372
## 98  18.79592 16.970430  8.739251 20.15552  9.256436 10.444694 10.814310
## 99  14.09206 12.763815 12.235026 19.78562 11.179115 11.697763 12.527439
## 100 18.86993 17.037959 10.412347 19.15029  9.342203  9.990472 11.061339
## 101 13.72277 12.100585  8.635025 19.64736  7.290007  8.052774  8.988102
## 102 14.14905 14.815813  9.722631 18.87126  8.935368 10.212263 10.256080
## 103 14.05901 11.611228  8.832139 17.72639  7.722895  8.278575  9.471053
## 104 16.14029 14.976568 10.745571 18.11004  9.538819 10.048293 10.673092
## 105 21.90772 20.337306 10.781881 19.84596  9.299197  9.821379 10.803154
## 106 12.87900 11.674964  8.890484 21.49815  8.237312  8.762589  9.752521
## 107 11.55303 11.659147  9.496184 20.05410  7.969417  8.572635  9.429172
## 108 15.30404 11.142649 11.492399 18.39825 10.125236 10.724162 11.321831
## 109 13.54572 11.447434 10.481360 18.73255  9.916628 10.691091 11.307609
## 110 25.69238 20.921377  9.314263 19.02520  8.686559  9.464939 10.506455
## 111 25.34770 20.689122  8.418992 24.76759  7.942093  9.095062  9.772552
## 112 25.74388 20.553644  8.419839 21.41300  6.748056  7.525599  8.790171
## 113 15.14489 15.490731  9.344918 21.30055  7.847138  8.507111  9.580745
## 114 12.26390  8.769283 10.113600 19.62256 10.336654 11.690665 10.798740
## 115 20.93232 18.051913  9.677846 16.56573  7.916451  8.172702  9.524207
## 116 17.73318 16.366598  8.701905 20.94546  7.665722  8.340444  9.444841
##         NCR1     PRF1    RETNLB     SOCS1   TICAM1      TNF
## 1   23.33234 27.53290 11.389996 13.025961 19.82281 21.01065
## 2   22.89312 26.26383  7.857130 10.292493 17.66099 22.36282
## 3   23.96486 25.98379  9.184355  9.205008 19.11736 22.81213
## 4   23.45405 23.24062  3.920192 10.692568 15.46167 18.96024
## 5   24.12714 27.09015  8.711133 10.586118 17.03506 24.77639
## 6   25.43377 27.84301 15.803676 10.037031 18.92915 25.01909
## 7   23.25482 23.54348 11.930951 10.137282 17.89026 20.40686
## 8   23.69673 28.00436 10.795116 10.187464 17.98634 21.91510
## 9   23.81112 27.02770 11.763447  9.833251 20.04689 25.99834
## 10  21.39968 20.45141  4.079604 11.242170 15.12650 18.21831
## 11  23.66060 26.21540 12.512554  8.390115 17.00279 24.39284
## 12  20.06957 21.01384  3.598778  8.892853 14.34632 18.18376
## 13  24.37670 25.10224 11.645965 10.674034 15.64940 20.93638
## 14  23.76296 27.17679 12.534258 11.718299 17.56715 20.51972
## 15  23.89841 24.64252 11.212956 10.034478 16.84957 22.49043
## 16  18.00615 22.71284  6.937463 10.044808 15.08446 17.05868
## 17  21.33841 26.20900  5.973854 10.589004 17.65482 19.35511
## 18  29.49340 25.66098 14.362461 10.790189 18.71333 23.10196
## 19  23.12706 25.41527  5.662282 10.323638 15.91257 19.63243
## 20  22.28476 23.09671  6.708141  9.097796 16.83769 19.32845
## 21  24.36829 25.16968  8.373846 10.727382 15.39051 19.76802
## 22  18.33988 22.09717  3.437346 10.438836 13.27494 18.82194
## 23  18.49862 22.59840  4.203089  9.684278 13.27406 22.08087
## 24  20.80061 24.78750  4.605416 10.017204 15.04402 23.69131
## 25  25.28210 25.69449  9.644582 12.041930 19.34746 26.10923
## 26  20.79229 26.11614  6.063100  9.980612 14.75829 18.44981
## 27  23.63638 28.71855 12.795983  9.838008 22.32244 29.87482
## 28  22.63025 28.71924 13.919183 15.560557 16.95622 20.43844
## 29  21.23689 29.16415  9.982388  9.482890 14.86344 21.08135
## 30  20.03371 21.10798  5.917482 11.194286 13.04953 16.84558
## 31  20.77055 28.50238 10.511800 11.429176 16.45653 24.00758
## 32  20.53246 29.53929 10.842803 12.155859 17.76277 21.50840
## 33  25.24495 25.38933 11.824440  9.660671 15.41369 20.83225
## 34  21.49756 25.56295  7.036342  8.830993 15.62009 20.40643
## 35  22.91124 26.37404 12.132540  9.535813 16.56348 22.45497
## 36  21.85548 28.15420  9.879838 11.097173 16.94929 24.08582
## 37  19.67674 21.14482  8.466972 12.429422 15.73371 16.46384
## 38  17.36359 18.08027  3.785109 10.974022 12.41300 13.78664
## 39  23.07639 24.78306  5.259263 10.307205 14.37050 19.70445
## 40  19.59789 26.08224 11.495341 11.487913 16.29785 20.60002
## 41  22.90715 27.33195  7.758496 11.493739 17.45863 22.01304
## 42  24.18657 29.51591  9.225770  9.632405 17.44476 21.63822
## 43  19.93898 20.38618  4.346449 11.657992 15.33566 15.86004
## 44  21.63473 28.11725  9.187486 10.970666 19.30253 21.39020
## 45  18.81508 21.15985  4.867295 11.106637 15.25927 16.16250
## 46  17.17266 21.54708  3.690941 10.228503 13.47368 18.09514
## 47  21.39350 23.41759  8.189116 10.139407 15.24493 17.65270
## 48  17.79122 25.89262  4.204721 10.569843 12.91764 17.50383
## 49  18.11990 19.92611  3.577107 10.324091 14.54200 16.01331
## 50  25.36659 26.01465 10.749170 11.024760 21.19794 21.01304
## 51  26.75319 27.09819  9.755923 10.478270 22.50241 21.56508
## 52  24.26265 27.12899 10.841208 12.989070 16.99448 20.03119
## 53  27.48604 27.18535  9.478791 10.607035 21.82549 21.98348
## 54  26.70705 25.41207 12.030827 13.123553 21.09282 21.07389
## 55  20.19892 21.79024  9.145387 10.833533 15.29784 19.01250
## 56  25.53460 26.41866  9.278296 11.303618 21.34152 21.31616
## 57  26.75811 25.29939  9.923647 11.828319 19.06670 21.78523
## 58  23.38357 26.14829  8.262385  8.890513 20.80948 23.70044
## 59  22.93460 25.11673  9.047844 11.867247 16.30778 18.36694
## 60  23.87387 28.88317  9.635853 12.837053 19.15818 22.89580
## 61  26.26590 23.85662  9.703631 11.627023 21.52452 22.65241
## 62  27.00035 27.90638 18.572389  8.318661 29.57724 23.13135
## 63  27.56776 29.56682 17.913556 10.084373 25.58661 27.81593
## 64  25.83862 27.59474 20.897073  9.482244 24.73153 27.59754
## 65  29.07498 28.19561 16.332964  9.665532 21.42768 23.91542
## 66  26.89351 28.51895  9.493187 11.330597 26.12898 26.67485
## 67  25.96444 28.22388 14.714598  8.041715 24.10520 28.93255
## 68  25.88560 25.43324 20.720319  7.087203 26.26861 27.69078
## 69  26.76586 28.06315 10.975465  9.162248 19.60484 29.13404
## 70  28.71996 31.76473 22.021335 13.581984 25.64584 28.37664
## 71  24.66802 26.78273 13.342864  8.919463 19.84097 25.19713
## 72  27.21686 26.69613 20.083060  7.158283 23.30618 28.17372
## 73  24.82501 28.62742 22.004654  8.871887 23.74250 28.31769
## 74  24.39808 26.19344  9.526062 11.755902 21.52548 20.79691
## 75  27.33835 28.89657 18.031914  9.895583 25.98677 28.62975
## 76  25.71248 29.79103 15.178442 10.393341 22.46358 27.50077
## 77  22.21427 25.55363  9.661429 12.013956 18.22035 19.84642
## 78  22.32535 24.21456  8.805372 11.222371 20.81727 19.51511
## 79  28.81700 26.48781 16.855267  8.065261 20.60755 23.72752
## 80  24.59491 26.21215  9.441200 11.032251 20.47256 20.23849
## 81  22.79706 23.15837  9.722345 12.451737 20.65242 17.12110
## 82  25.50603 26.79236 13.220426 14.919748 20.57782 22.50920
## 83  26.55833 27.50508 10.058471 12.308402 22.04861 23.31357
## 84  27.12947 24.06304 10.004453 11.728596 24.10621 21.72844
## 85  24.81094 23.62678  9.365788 11.073955 17.51909 19.97281
## 86  22.33892 23.77440  9.192797 11.689073 18.09591 20.75239
## 87  23.72726 23.93945 15.923781  7.757090 27.17214 25.43076
## 88  25.57028 25.88464 11.281775 12.009186 21.10553 22.98131
## 89  24.28717 25.98337 10.982572 12.330284 20.82249 21.80276
## 90  23.38032 25.16243 10.944685 11.589537 23.26258 20.96380
## 91  22.97322 27.10505  8.885230 11.076750 16.22269 18.16059
## 92  24.09515 24.29967 10.008384 10.705349 22.69601 20.23189
## 93  26.18841 25.49433 10.995071 10.401308 20.49431 21.47300
## 94  19.15281 19.06725  7.547668 11.758377 17.68614 16.26408
## 95  23.92244 25.49845  9.999622  9.756697 21.55859 21.56077
## 96  25.46251 26.64396  9.906633 10.453336 22.37117 22.84888
## 97  22.09169 28.07379  9.665312  9.605007 20.07661 20.12716
## 98  26.34456 23.49281  9.661908  9.225406 21.56845 22.03168
## 99  24.33736 23.94627 12.576263 12.867410 20.08692 20.32090
## 100 24.35790 24.18033 11.724569 10.427714 27.52816 26.40631
## 101 26.30723 23.92448  7.985537  9.284697 19.19948 18.33022
## 102 22.18252 28.09646  5.846103 10.089456 18.04099 19.42994
## 103 26.67078 22.17432  8.544338  9.708748 19.40001 18.85483
## 104 24.40765 25.97794 11.387807 11.285548 22.13123 21.26313
## 105 24.99690 28.24536 10.488427 11.445105 22.62902 25.72357
## 106 21.39041 22.26847 10.541307  9.678177 16.57747 17.42194
## 107 23.67079 24.50357  8.477857 10.166476 17.26136 17.46136
## 108 19.99873 23.04488 10.740533 11.861010 20.43164 17.80555
## 109 29.54948 22.51975 10.484425 12.108359 20.85134 16.34527
## 110 23.17187 25.18772 11.220518  9.548208 22.43535 22.25967
## 111 21.21622 24.28532 10.960531  8.968065 23.16068 23.48999
## 112 22.99591 24.24964 10.019037  8.531568 21.37614 20.82249
## 113 25.42788 24.87718  8.590075  8.871211 17.57769 19.73392
## 114 23.75737 20.88617  6.927890 13.408973 17.53509 16.57800
## 115 26.10599 28.46406 10.098975 10.393635 23.04097 22.15808
## 116 24.42321 27.33021 10.388094  8.474758 19.93831 21.28205

Caution: When imputing data, the percentages of inertia associated with the first dimensions will be overestimated.

Another problem: the imputed data are, when the pca is performed considered like real observations. But they are estimations!!

Visualizing uncertainty due to issing data:

–> mulrimple imputation: generate several plausible values for each missing data point

We here visualize the variability, that is uncertainty on the plane defined by two pca axes.

## $PlotIndProc

## 
## $PlotDim

## 
## $PlotIndSupp

## 
## $PlotVar

Individuals lying on the axis have no missing data, but individuals that far away have many missing data. big ellipse = big uncertainty tight elipse (line) = low uncertainty

Variable representation: Poins tight together )look like one) - have no missing variables –> low uncertainty Points spread – > higher variability – > higher uncertainty

High uncertainty–> we should interpret the result with care

The individuals with many missing data values make the axes move, and thus the positions of all individuals

Therefore in the last plots every individual is getting an eclipse as they are as well influenced by the missing data of the others.

THe plot with the dimensions shows the projections of the pca dimensions of each imputed table on the pca plane obtained using the original imputed data table

As all of the arrows are close to either the first or second axes, this means that the axes are stable with respect to the set of imputed tables –> we don’t have evidence of instability here.

## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa

## 
## Attaching package: 'MASS'

## The following object is masked from 'package:dplyr':
## 
##     select

## 
## Attaching package: 'reshape2'

## The following object is masked from 'package:tidyr':
## 
##     smiths

The function fviz_contrib() [factoextra package] can be used to draw a bar plot of variable contributions. If your data contains many variables, you can decide to show only the top contributing variables. The R code below shows the top 10 variables contributing to the principal components: The total contribution to PC1 and PC2 is obtained with the following R code: The red dashed line on the graph above indicates the expected average contribution. If the contribution of the variables were uniform, the expected value would be 1/length(variables) = 1/10 = 10%. For a given component, a variable with a contribution larger than this cutoff could be considered as important in contributing to the component.

Note that, the total contribution of a given variable, on explaining the variations retained by two principal components, say PC1 and PC2, is calculated as contrib = [(C1 * Eig1) + (C2 * Eig2)]/(Eig1 + Eig2), where

C1 and C2 are the contributions of the variable on PC1 and PC2, respectively Eig1 and Eig2 are the eigenvalues of PC1 and PC2, respectively. Recall that eigenvalues measure the amount of variation retained by each PC. In this case, the expected average contribution (cutoff) is calculated as follow: As mentioned above, if the contributions of the 10 variables were uniform, the expected average contribution on a given PC would be 1/10 = 10%. The expected average contribution of a variable for PC1 and PC2 is : [(10* Eig1) + (10 * Eig2)]/(Eig1 + Eig2)

To visualize the contribution of individuals to the first two principal components, type this:

PCA + Biplot combination In the following example, we want to color both individuals and variables by groups. The trick is to use pointshape = 21 for individual points. This particular point shape can be filled by a color using the argument fill.ind. The border line color of individual points is set to “black” using col.ind. To color variable by groups, the argument col.var will be used.

To customize individuals and variable colors, we use the helper functions fill_palette() and color_palette() [in ggpubr package].

## 
## Call:
## lm(formula = max_WL ~ pc1 + pc2 + challenge_infection, data = g)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -14.4027  -3.0794   0.1195   3.5224  14.2423 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             91.7467     0.7956 115.324  < 2e-16 ***
## pc1                      0.1173     0.1829   0.641 0.522840    
## pc2                     -0.7231     0.2842  -2.544 0.012337 *  
## challenge_infectionE88  -6.1131     1.4142  -4.323 3.38e-05 ***
## challenge_infectionUNI   4.4111     1.1359   3.883 0.000175 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.258 on 111 degrees of freedom
## Multiple R-squared:  0.3805, Adjusted R-squared:  0.3582 
## F-statistic: 17.04 on 4 and 111 DF,  p-value: 6.364e-11

## [1] 721.1667

## 
## Call:
## lm(formula = max_WL ~ pc1 + pc2, data = g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -17.122  -3.155   1.295   4.938  10.348 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  92.3746     0.6026 153.288   <2e-16 ***
## pc1           0.1256     0.2147   0.585   0.5599    
## pc2          -0.7165     0.3468  -2.066   0.0411 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.49 on 113 degrees of freedom
## Multiple R-squared:  0.0392, Adjusted R-squared:  0.0222 
## F-statistic: 2.305 on 2 and 113 DF,  p-value: 0.1044

##                    df      AIC
## weight_lm           6 721.1667
## weight_lm_exp_only  4 768.0702

repeating the heatmap on the now imputed data

### Prepare the annotation data frame for the heatmap
annotation_df <- g %>%
  dplyr::select(c("EH_ID", "Parasite_challenge", "infection_history"))

### Data tidying for the heatmap function
gene_imp <- g %>% left_join(imputed_gene, by = c("pc1", "pc2"))

#remove all columns of the non-imputed data
gene_imp = gene_imp[,!grepl(".x$",names(gene_imp))]

#remove the suffix y
gene_imp <- gene_imp %>% rename_with(~str_remove(., '.y'))

gene <- gene_imp %>% dplyr::select(c("EH_ID", "CXCR3_bio", "IL.6",
  "IL.10", "IL.13", "IL.10", "IL.13", "IL1RN", "CASP1", "CXCL9", 
  "IDO1", "IRGM1", "MPO", "MUC2", "MUC5AC", "MYD88", 
  "NCR1", "PRF1", "RETNLB", "SOCS1", "TICAM1", "TNF"))
 
 # turn the data frame into a matrix and transpose it. We want to have each cell 
 # type as a row name 
 gene <- t(as.matrix(gene))
 
 #switch the matrix back to a data frame format
 gene <- as.data.frame(gene)
 
 # turn the first row into column names
 gene %>%
     row_to_names(row_number = 1) -> gene
 
 # Now further prepare the data frame for plotting by removing the first row
 ## and convert the column to row names with the cells 
 gene[-1, ] -> heatmap_data
 
 
 table(rowSums(is.na(heatmap_data)) == nrow(heatmap_data))

## 
## FALSE 
##    18

 # turn the columns to numeric other wise the heatmap function will not work
 heatmap_data[] <- lapply(heatmap_data, function(x) as.numeric(as.character(x)))

 # remove columns with only NAs 
 heatmap_data <- Filter(function(x)!all(is.na(x)), heatmap_data) 
 
 #remove rows with only Nas
 heatmap_data <-  heatmap_data[, colSums(is.na(heatmap_data)) != nrow(heatmap_data)]

rownames(annotation_df) <- colnames(heatmap_data)

Heatmap on gene expression data:



################################### FACS #######################################

Note: Using an external vector in selections is ambiguous.

ℹ Use `all_of(CellCount.cols)` instead of `CellCount.cols` to silence this message.

ℹ See https://tidyselect.r-lib.org/reference/faq-external-vector.html.

This message is displayed once per session.


<img src="Gene_expression_total_files/figure-html/unnamed-chunk-33-1.png" width="672" />
<img src="Gene_expression_total_files/figure-html/unnamed-chunk-34-1.png" width="672" />
<img src="Gene_expression_total_files/figure-html/unnamed-chunk-35-1.png" width="672" />
<img src="Gene_expression_total_files/figure-html/unnamed-chunk-36-1.png" width="672" />


<img src="Gene_expression_total_files/figure-html/unnamed-chunk-37-1.png" width="672" />


###### PCA FACS ###################################################################

Adding missing grouping variables: `infection`


<img src="Gene_expression_total_files/figure-html/unnamed-chunk-39-1.png" width="672" />


<img src="Gene_expression_total_files/figure-html/unnamed-chunk-40-1.png" width="672" />

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Warning: Removed 66 rows containing non-finite values (stat_bin).


<img src="Gene_expression_total_files/figure-html/unnamed-chunk-41-1.png" width="672" />

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Warning: Removed 46 rows containing non-finite values (stat_bin).


<img src="Gene_expression_total_files/figure-html/unnamed-chunk-41-2.png" width="672" />

EH_ID primary_infection challenge_infection infection_history

Length:85 Length:85 Length:85 Length:85

Class :character Class :character Class :character Class :character

Mode :character Mode :character Mode :character Mode :character

mouse_strain hybrid_status Parasite_primary Parasite_challenge

Length:85 Length:85 Length:85 Length:85

Class :character Class :character Class :character Class :character

Mode :character Mode :character Mode :character Mode :character

max_WL max_OOC Position CD4

Min. : 73.45 Min. : 0 Length:85 Min. :14.60

1st Qu.: 89.78 1st Qu.: 0 Class :character 1st Qu.:42.86

Median : 94.58 Median : 72500 Mode :character Median :48.49

Mean : 92.96 Mean : 260500 Mean :46.52

3rd Qu.: 97.72 3rd Qu.: 317500 3rd Qu.:53.40

Max. :100.00 Max. :1447500 Max. :68.01

Treg Div_Treg Treg17 Th1

Min. : 3.245 Min. : 6.48 Min. : 3.230 Min. : 1.235

1st Qu.: 5.650 1st Qu.:16.00 1st Qu.: 7.520 1st Qu.: 2.505

Median : 7.005 Median :22.15 Median : 9.505 Median : 4.380

Mean : 8.382 Mean :23.41 Mean :10.646 Mean : 4.822

3rd Qu.: 9.840 3rd Qu.:30.20 3rd Qu.:13.400 3rd Qu.: 6.795

Max. :27.230 Max. :52.62 Max. :25.480 Max. :13.100

Div_Th1 Th17 Div_Th17 CD8

Min. :11.91 Min. :0.485 Min. : 9.20 Min. : 5.79

1st Qu.:27.50 1st Qu.:0.925 1st Qu.:24.22 1st Qu.:12.14

Median :51.87 Median :1.185 Median :33.08 Median :16.40

Mean :47.54 Mean :1.390 Mean :36.65 Mean :18.24

3rd Qu.:63.50 3rd Qu.:1.520 3rd Qu.:46.88 3rd Qu.:25.30

Max. :90.78 Max. :4.810 Max. :78.31 Max. :35.58

Act_CD8 Div_Act_CD8 IFNy_CD4 IFNy_CD8

Min. : 2.365 Min. : 6.07 Min. : 0.340 Min. : 3.605

1st Qu.: 6.720 1st Qu.:12.07 1st Qu.: 1.670 1st Qu.:13.490

Median :10.700 Median :29.02 Median : 2.580 Median :19.390

Mean :10.927 Mean :29.06 Mean : 3.358 Mean :20.395

3rd Qu.:13.700 3rd Qu.:43.53 3rd Qu.: 3.910 3rd Qu.:26.830

Max. :27.805 Max. :62.70 Max. :12.910 Max. :50.100

Treg_prop IL17A_CD4 delta

Min. :72.66 Min. :0.1650 Min. :-12.690

1st Qu.:91.25 1st Qu.:0.3850 1st Qu.: -8.791

Median :93.28 Median :0.5075 Median : -6.840

Mean :92.62 Mean :0.6732 Mean : -5.561

3rd Qu.:94.68 3rd Qu.:0.7825 3rd Qu.: -4.750

Max. :96.64 Max. :2.3400 Max. : 11.610

NA’s :33 NA’s :33 NA’s :3

Warning in par(usr): argument 1 does not name a graphical parameter


<img src="Gene_expression_total_files/figure-html/unnamed-chunk-43-1.png" width="672" />

Warning in PCA(f[12:26]): Missing values are imputed by the mean of the

variable: you should use the imputePCA function of the missMDA package


<img src="Gene_expression_total_files/figure-html/unnamed-chunk-44-1.png" width="672" /><img src="Gene_expression_total_files/figure-html/unnamed-chunk-44-2.png" width="672" />
<img src="Gene_expression_total_files/figure-html/unnamed-chunk-45-1.png" width="672" /><img src="Gene_expression_total_files/figure-html/unnamed-chunk-45-2.png" width="672" />

We will now continue by using an iterative pca to impute missing data 
 A. Initialization: impute using the mean
 B. Step lampda:
  # a. do pca on imputed data table S dimensions retained
  # b. missing data imputed using pca
  # c. means (and standard deviations) updated
 C. Iterate the estimation and imputation steps (until convergence)
(convergence: the act of converging and especially moving toward union or uniformity)

Overfitting is a common problem due to believing too much in links between variables. 
--> regularized iterative PCA (This version is what is being implented in missMDA)
This is a way of taking less risk when imputing the missing data. 
The algorithm estimates the missing data values with values that have no influence
on the PCA results, i.e., no influence on the coordinates of the individals or variables.

CD4 Treg Div_Treg Treg17 Th1 Div_Th1 Th17 Div_Th17 CD8 Act_CD8

1 44.900 6.385 16.205 13.520 6.780 71.200 0.890 46.875 14.390 11.500

2 46.145 7.005 21.365 11.565 10.920 75.115 1.075 42.390 13.840 13.205

3 56.220 7.150 12.455 9.505 2.965 19.840 1.630 30.055 10.020 10.915

4 40.590 6.450 23.760 12.780 9.250 81.210 1.705 78.305 25.305 11.105

5 52.245 8.695 13.465 14.400 2.545 27.850 1.060 27.445 17.550 9.815

6 46.895 6.890 13.355 7.035 2.900 25.520 0.695 32.195 7.490 5.395

Div_Act_CD8 IFNy_CD4 IFNy_CD8 Treg_prop IL17A_CD4

1 49.520 4.915 21.740 93.605 0.415

2 59.090 9.085 27.535 92.970 0.385

3 11.535 3.045 41.360 92.845 0.575

4 55.935 9.085 38.165 93.505 0.850

5 12.830 2.005 19.390 91.305 0.250

6 21.310 2.795 19.230 93.110 0.270


<img src="Gene_expression_total_files/figure-html/unnamed-chunk-46-1.png" width="672" /><img src="Gene_expression_total_files/figure-html/unnamed-chunk-46-2.png" width="672" />

Call:

PCA(X = comp$completeObs)

Eigenvalues

Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 Dim.6 Dim.7

Variance 5.994 2.576 1.814 1.160 0.702 0.605 0.501

% of var. 39.958 17.172 12.094 7.734 4.677 4.033 3.343

Cumulative % of var. 39.958 57.130 69.224 76.958 81.635 85.668 89.011

Dim.8 Dim.9 Dim.10 Dim.11 Dim.12 Dim.13 Dim.14

Variance 0.411 0.392 0.312 0.210 0.142 0.076 0.055

% of var. 2.739 2.617 2.079 1.397 0.944 0.506 0.369

Cumulative % of var. 91.750 94.366 96.446 97.843 98.787 99.293 99.662

Dim.15

Variance 0.051

% of var. 0.338

Cumulative % of var. 100.000

Individuals

Dist Dim.1 ctr cos2 Dim.2 ctr cos2 Dim.3

1 | 2.569 | 0.703 0.097 0.075 | 1.347 0.828 0.275 | 0.776

2 | 4.021 | 2.154 0.910 0.287 | 2.020 1.863 0.252 | 0.598

3 | 3.353 | -1.260 0.312 0.141 | 1.268 0.734 0.143 | -1.746

4 | 4.841 | 3.307 2.147 0.467 | 1.745 1.390 0.130 | 0.850

5 | 2.599 | -1.923 0.726 0.547 | 0.177 0.014 0.005 | -0.872

6 | 2.925 | -1.988 0.776 0.462 | 0.681 0.212 0.054 | -1.101

7 | 4.194 | 1.797 0.634 0.184 | 2.784 3.539 0.441 | 0.822

8 | 3.597 | 1.077 0.228 0.090 | 2.009 1.844 0.312 | -0.161

9 | 5.264 | 2.557 1.284 0.236 | 3.649 6.083 0.481 | -0.320

10 | 3.373 | 1.552 0.473 0.212 | -0.442 0.089 0.017 | -0.292

11 | 4.153 | -3.671 2.646 0.782 | 1.374 0.863 0.110 | 0.226

12 | 3.819 | 2.096 0.862 0.301 | 0.229 0.024 0.004 | -0.176

13 | 3.468 | -3.110 1.898 0.804 | 0.115 0.006 0.001 | -1.211

14 | 4.715 | 1.860 0.679 0.156 | 3.043 4.229 0.417 | 1.612

15 | 1.937 | -0.319 0.020 0.027 | 0.971 0.431 0.251 | 0.681

16 | 3.364 | -3.278 2.109 0.949 | 0.173 0.014 0.003 | 0.041

17 | 3.622 | -3.225 2.041 0.793 | -0.778 0.276 0.046 | -0.768

18 | 5.427 | 4.087 3.278 0.567 | -0.336 0.051 0.004 | -0.648

19 | 3.055 | -2.562 1.288 0.703 | 1.026 0.481 0.113 | -0.789

20 | 2.668 | -1.145 0.258 0.184 | 1.764 1.421 0.437 | 0.849

21 | 3.219 | -0.358 0.025 0.012 | -1.855 1.572 0.332 | 0.653

22 | 2.150 | 0.362 0.026 0.028 | 0.965 0.426 0.202 | 0.096

23 | 2.817 | 0.430 0.036 0.023 | 0.905 0.374 0.103 | 1.921

24 | 3.925 | -3.062 1.841 0.609 | -1.466 0.982 0.139 | -1.499

25 | 8.816 | 4.107 3.311 0.217 | -5.619 14.421 0.406 | -2.451

26 | 1.424 | -0.626 0.077 0.193 | -0.024 0.000 0.000 | 0.277

27 | 3.504 | -2.441 1.170 0.485 | -1.588 1.152 0.205 | -1.421

28 | 3.573 | -0.084 0.001 0.001 | -0.162 0.012 0.002 | 3.146

29 | 3.849 | -3.125 1.917 0.659 | 0.543 0.135 0.020 | -0.104

30 | 4.024 | -3.604 2.549 0.802 | 1.154 0.609 0.082 | -0.199

31 | 3.499 | 0.140 0.004 0.002 | 2.589 3.062 0.548 | 1.091

32 | 3.424 | 1.024 0.206 0.089 | 0.739 0.250 0.047 | 2.335

33 | 3.565 | -1.846 0.669 0.268 | -0.690 0.217 0.037 | -1.077

34 | 2.886 | -2.359 1.092 0.668 | 0.063 0.002 0.000 | 0.297

35 | 3.571 | -2.472 1.199 0.479 | 0.886 0.358 0.062 | 1.008

36 | 2.842 | 0.564 0.062 0.039 | -1.401 0.897 0.243 | -0.966

37 | 2.930 | -0.622 0.076 0.045 | 0.718 0.236 0.060 | 0.294

38 | 4.022 | -3.789 2.817 0.887 | -0.116 0.006 0.001 | 0.524

39 | 3.913 | -3.579 2.514 0.836 | -0.929 0.394 0.056 | 0.230

40 | 6.692 | 4.507 3.986 0.453 | -2.681 3.283 0.161 | 2.323

41 | 3.489 | -0.685 0.092 0.039 | 1.648 1.241 0.223 | 2.202

42 | 2.620 | -1.777 0.620 0.460 | 0.868 0.344 0.110 | -0.168

43 | 3.272 | -2.343 1.077 0.513 | -2.029 1.880 0.385 | -0.130

44 | 2.977 | -0.524 0.054 0.031 | -1.163 0.618 0.153 | 2.218

45 | 4.040 | 0.647 0.082 0.026 | -2.611 3.114 0.418 | 2.299

46 | 4.210 | -3.437 2.318 0.666 | -2.081 1.978 0.244 | -0.219

47 | 3.963 | 1.534 0.462 0.150 | -2.668 3.250 0.453 | 1.908

48 | 4.232 | -3.652 2.618 0.745 | -1.590 1.154 0.141 | 0.176

49 | 4.676 | -4.032 3.191 0.743 | 1.425 0.927 0.093 | 0.620

50 | 4.549 | 2.652 1.381 0.340 | -2.417 2.668 0.282 | 1.692

51 | 3.102 | -2.702 1.433 0.759 | -1.283 0.751 0.171 | 0.009

52 | 4.471 | 1.400 0.385 0.098 | -2.790 3.554 0.389 | 2.023

53 | 5.476 | 4.048 3.216 0.546 | 0.748 0.256 0.019 | -2.923

54 | 5.869 | 5.049 5.003 0.740 | 1.450 0.960 0.061 | -1.937

55 | 2.600 | 1.610 0.509 0.383 | -0.102 0.005 0.002 | -1.172

56 | 3.776 | -1.406 0.388 0.139 | -0.987 0.445 0.068 | -2.673

57 | 5.306 | 4.215 3.487 0.631 | 1.028 0.483 0.038 | -2.630

58 | 4.875 | 3.322 2.167 0.465 | 0.839 0.322 0.030 | -1.975

59 | 5.209 | 3.719 2.714 0.510 | 1.797 1.475 0.119 | -2.467

60 | 4.694 | -0.537 0.057 0.013 | 0.662 0.200 0.020 | -2.496

61 | 6.400 | 4.561 4.084 0.508 | 1.699 1.319 0.071 | -2.170

62 | 2.547 | -0.713 0.100 0.078 | 0.562 0.144 0.049 | -1.182

63 | 5.259 | 4.140 3.365 0.620 | -0.827 0.312 0.025 | 0.924

64 | 5.749 | 4.308 3.643 0.562 | -2.073 1.963 0.130 | 0.453

65 | 4.316 | -2.161 0.917 0.251 | -1.059 0.512 0.060 | -2.845

66 | 2.171 | -0.336 0.022 0.024 | 1.249 0.712 0.331 | 0.756

67 | 3.622 | -3.204 2.015 0.782 | 0.907 0.375 0.063 | 0.629

68 | 2.481 | 0.853 0.143 0.118 | 1.135 0.589 0.209 | 1.465

69 | 2.346 | 0.554 0.060 0.056 | -1.594 1.161 0.462 | -0.491

70 | 2.379 | 0.063 0.001 0.001 | 1.238 0.700 0.271 | 0.611

71 | 2.952 | 0.605 0.072 0.042 | -2.260 2.332 0.586 | 0.449

72 | 2.560 | 1.167 0.267 0.208 | -1.781 1.448 0.484 | 0.293

73 | 2.599 | 0.796 0.124 0.094 | -1.907 1.661 0.538 | -0.290

74 | 3.656 | 2.789 1.527 0.582 | 0.089 0.004 0.001 | 1.502

75 | 5.314 | 1.302 0.333 0.060 | 0.928 0.393 0.030 | -0.407

76 | 2.777 | 0.163 0.005 0.003 | 0.568 0.147 0.042 | 1.782

77 | 2.100 | 0.776 0.118 0.136 | 0.053 0.001 0.001 | 1.033

78 | 2.954 | 1.793 0.631 0.368 | 1.045 0.499 0.125 | 0.243

79 | 4.264 | 2.565 1.291 0.362 | -2.916 3.883 0.468 | 0.519

80 | 2.362 | -1.315 0.339 0.310 | -1.381 0.871 0.342 | -0.990

81 | 2.617 | -1.079 0.229 0.170 | -1.844 1.553 0.496 | -0.816

82 | 3.130 | -2.954 1.712 0.891 | 0.124 0.007 0.002 | -0.209

83 | 1.640 | -0.019 0.000 0.000 | 1.059 0.512 0.417 | 0.201

84 | 3.203 | -2.546 1.272 0.632 | 0.447 0.091 0.019 | -1.094

85 | 1.701 | -1.084 0.231 0.406 | 0.941 0.404 0.306 | 0.426

ctr cos2

1 0.391 0.091 |

2 0.232 0.022 |

3 1.978 0.271 |

4 0.469 0.031 |

5 0.494 0.113 |

6 0.786 0.142 |

7 0.438 0.038 |

8 0.017 0.002 |

9 0.066 0.004 |

10 0.055 0.007 |

11 0.033 0.003 |

12 0.020 0.002 |

13 0.950 0.122 |

14 1.686 0.117 |

15 0.301 0.124 |

16 0.001 0.000 |

17 0.383 0.045 |

18 0.273 0.014 |

19 0.403 0.067 |

20 0.467 0.101 |

21 0.277 0.041 |

22 0.006 0.002 |

23 2.393 0.465 |

24 1.457 0.146 |

25 3.895 0.077 |

26 0.050 0.038 |

27 1.309 0.164 |

28 6.419 0.775 |

29 0.007 0.001 |

30 0.026 0.002 |

31 0.772 0.097 |

32 3.537 0.465 |

33 0.752 0.091 |

34 0.057 0.011 |

35 0.659 0.080 |

36 0.605 0.115 |

37 0.056 0.010 |

38 0.178 0.017 |

39 0.034 0.003 |

40 3.499 0.120 |

41 3.145 0.398 |

42 0.018 0.004 |

43 0.011 0.002 |

44 3.189 0.555 |

45 3.428 0.324 |

46 0.031 0.003 |

47 2.362 0.232 |

48 0.020 0.002 |

49 0.249 0.018 |

50 1.856 0.138 |

51 0.000 0.000 |

52 2.654 0.205 |

53 5.542 0.285 |

54 2.434 0.109 |

55 0.890 0.203 |

56 4.632 0.501 |

57 4.486 0.246 |

58 2.530 0.164 |

59 3.945 0.224 |

60 4.040 0.283 |

61 3.053 0.115 |

62 0.906 0.215 |

63 0.554 0.031 |

64 0.133 0.006 |

65 5.248 0.434 |

66 0.371 0.121 |

67 0.256 0.030 |

68 1.392 0.349 |

69 0.156 0.044 |

70 0.242 0.066 |

71 0.131 0.023 |

72 0.056 0.013 |

73 0.054 0.012 |

74 1.463 0.169 |

75 0.108 0.006 |

76 2.060 0.412 |

77 0.692 0.242 |

78 0.038 0.007 |

79 0.174 0.015 |

80 0.636 0.176 |

81 0.432 0.097 |

82 0.028 0.004 |

83 0.026 0.015 |

84 0.776 0.117 |

85 0.118 0.063 |

Variables

Dim.1 ctr cos2 Dim.2 ctr cos2 Dim.3 ctr

CD4 | -0.795 10.539 0.632 | 0.047 0.085 0.002 | 0.397 8.675

Treg | 0.526 4.608 0.276 | -0.513 10.228 0.263 | -0.510 14.355

Div_Treg | 0.618 6.364 0.381 | -0.095 0.354 0.009 | 0.514 14.566

Treg17 | 0.079 0.104 0.006 | 0.656 16.710 0.430 | 0.342 6.446

Th1 | 0.870 12.638 0.757 | 0.291 3.291 0.085 | -0.053 0.155

Div_Th1 | 0.828 11.428 0.685 | -0.082 0.259 0.007 | 0.405 9.055

Th17 | 0.572 5.458 0.327 | 0.149 0.864 0.022 | -0.430 10.185

Div_Th17 | 0.743 9.217 0.552 | 0.027 0.029 0.001 | 0.059 0.193

CD8 | -0.185 0.574 0.034 | -0.706 19.354 0.499 | 0.236 3.066

Act_CD8 | 0.758 9.587 0.575 | -0.286 3.179 0.082 | 0.041 0.091

Div_Act_CD8 | 0.644 6.911 0.414 | 0.092 0.327 0.008 | 0.566 17.659

IFNy_CD4 | 0.621 6.436 0.386 | 0.553 11.862 0.306 | -0.177 1.720

IFNy_CD8 | 0.536 4.800 0.288 | 0.532 10.984 0.283 | -0.312 5.377

Treg_prop | -0.549 5.032 0.302 | 0.686 18.279 0.471 | 0.221 2.691

IL17A_CD4 | 0.615 6.305 0.378 | -0.329 4.196 0.108 | 0.323 5.768

cos2

CD4 0.157 |

Treg 0.260 |

Div_Treg 0.264 |

Treg17 0.117 |

Th1 0.003 |

Div_Th1 0.164 |

Th17 0.185 |

Div_Th17 0.003 |

CD8 0.056 |

Act_CD8 0.002 |

Div_Act_CD8 0.320 |

IFNy_CD4 0.031 |

IFNy_CD8 0.098 |

Treg_prop 0.049 |

IL17A_CD4 0.105 |

NULL

$Dim.1

$quanti

correlation p.value

Th1 0.8703175 2.943471e-27

Div_Th1 0.8276238 1.596060e-22

Act_CD8 0.7580283 4.492673e-17

Div_Th17 0.7432721 3.764290e-16

Div_Act_CD8 0.6436080 3.063061e-11

IFNy_CD4 0.6210972 2.270480e-10

Div_Treg 0.6175982 3.056079e-10

IL17A_CD4 0.6147436 3.883906e-10

Th17 0.5719412 1.079666e-08

IFNy_CD8 0.5363617 1.218142e-07

Treg 0.5255354 2.412497e-07

Treg_prop -0.5491894 5.252093e-08

CD4 -0.7947807 1.087537e-19

attr(,“class”)

[1] “condes” “list”

$Dim.2

$quanti

correlation p.value

Treg_prop 0.6861704 4.249117e-13

Treg17 0.6560699 9.395061e-12

IFNy_CD4 0.5527736 4.125555e-08

IFNy_CD8 0.5319037 1.618660e-07

Th1 0.2911745 6.859347e-03

Act_CD8 -0.2861658 7.930276e-03

IL17A_CD4 -0.3287530 2.126702e-03

Treg -0.5132770 5.084715e-07

CD8 -0.7060657 4.440787e-14

attr(,“class”)

[1] “condes” “list”

$Dim.3

$quanti

correlation p.value

Div_Act_CD8 0.5660036 1.650437e-08

Div_Treg 0.5140433 4.857300e-07

Div_Th1 0.4053084 1.191116e-04

CD4 0.3967074 1.707468e-04

Treg17 0.3419675 1.358813e-03

IL17A_CD4 0.3234929 2.528223e-03

CD8 0.2358423 2.978669e-02

Treg_prop 0.2209303 4.216380e-02

IFNy_CD8 -0.3123195 3.614568e-03

Th17 -0.4298530 4.032726e-05

Treg -0.5103074 6.064786e-07

attr(,“class”)

[1] “condes” “list”

$call

$call$num.var

[1] 1

$call$proba

[1] 0.05

$call$weights

[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

[39] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

[77] 1 1 1 1 1 1 1 1 1

$call$X

Dim.1 CD4 Treg Div_Treg Treg17 Th1 Div_Th1 Th17 Div_Th17

1 0.70274005 44.900 6.385 16.205 13.520 6.780 71.200 0.890 46.875

2 2.15371252 46.145 7.005 21.365 11.565 10.920 75.115 1.075 42.390

3 -1.26014366 56.220 7.150 12.455 9.505 2.965 19.840 1.630 30.055

4 3.30719107 40.590 6.450 23.760 12.780 9.250 81.210 1.705 78.305

5 -1.92290606 52.245 8.695 13.465 14.400 2.545 27.850 1.060 27.445

6 -1.98830926 46.895 6.890 13.355 7.035 2.900 25.520 0.695 32.195

7 1.79693547 49.470 6.065 24.795 13.950 6.870 76.515 1.110 65.735

8 1.07715289 45.740 6.520 17.115 8.645 9.585 51.870 1.090 40.600

9 2.55734813 46.330 6.465 21.000 14.540 7.020 67.360 1.615 65.055

10 1.55244898 43.325 8.915 13.090 6.825 7.710 79.020 1.185 55.835

11 -3.67145719 68.010 3.630 14.110 14.350 1.730 14.310 0.925 33.075

12 2.09560967 37.435 9.045 20.515 9.260 9.100 64.370 0.805 49.910

13 -3.10976423 53.250 6.895 7.850 9.015 2.505 19.190 0.945 28.815

14 1.86015854 43.090 6.120 21.885 25.480 7.620 60.780 1.415 45.325

15 -0.31936731 47.340 6.465 16.775 13.315 4.840 54.635 0.970 35.275

16 -3.27796278 61.525 5.650 12.710 9.660 1.875 29.575 0.535 21.155

17 -3.22466411 51.475 6.690 12.110 7.535 1.455 21.435 0.550 22.920

18 4.08663732 36.155 8.875 24.110 8.970 11.540 90.780 4.050 67.780

19 -2.56184560 54.575 5.110 13.005 9.130 1.835 22.300 1.075 30.890

20 -1.14545502 58.920 5.075 16.575 15.280 4.445 48.205 0.795 28.355

21 -0.35784719 49.925 7.915 15.795 4.280 3.710 75.720 0.765 46.720

22 0.36155681 43.270 7.155 16.365 10.450 5.485 65.425 1.170 29.270

23 0.42967182 53.240 5.215 41.605 11.280 6.795 59.590 1.225 36.960

24 -3.06239988 49.350 9.015 8.260 4.370 1.535 22.665 0.580 19.230

25 4.10716064 28.295 27.230 38.210 8.875 5.925 65.700 1.935 41.455

26 -0.62642215 53.270 6.670 23.525 7.885 3.540 53.200 1.360 24.045

27 -2.44132737 54.265 9.475 10.550 4.220 1.425 22.075 1.070 31.665

28 -0.08383332 48.490 5.220 36.585 17.105 3.370 72.075 0.620 38.040

29 -3.12519296 56.780 4.835 16.835 13.005 1.735 11.910 1.120 50.070

30 -3.60357020 67.430 3.900 13.000 12.720 1.855 13.035 1.440 19.120

31 0.14034538 53.510 4.525 30.580 17.135 5.615 41.680 0.975 22.355

32 1.02365226 49.935 6.265 43.855 12.800 5.395 56.130 0.875 30.460

33 -1.84647089 42.860 8.465 8.225 10.045 1.780 31.145 1.110 63.235

34 -2.35893055 55.305 7.315 22.150 12.340 1.835 34.180 1.010 12.905

35 -2.47171941 52.100 5.205 31.795 18.210 2.740 21.990 0.730 27.275

36 0.56421071 48.705 11.315 19.245 7.590 3.110 35.555 1.435 39.995

37 -0.62240660 42.070 5.530 31.595 9.750 3.055 29.220 0.840 30.170

38 -3.78862046 55.005 4.635 17.730 12.165 1.510 28.170 0.660 9.700

39 -3.57899552 55.135 4.955 19.550 6.445 1.305 27.140 0.485 19.200

40 4.50656799 48.920 14.300 52.620 14.605 7.425 79.505 1.730 69.700

41 -0.68478037 60.705 3.740 36.475 18.505 5.280 48.670 1.940 24.220

42 -1.77666382 49.850 4.700 26.940 8.940 3.020 22.305 1.400 21.805

43 -2.34270897 53.755 9.235 19.495 6.415 1.375 27.520 0.830 28.285

44 -0.52448739 48.380 6.965 36.775 9.390 4.130 60.855 0.680 27.710

45 0.64652384 46.695 9.310 34.995 6.330 2.810 76.265 0.635 46.690

46 -3.43662451 58.170 7.095 12.905 5.325 1.330 22.600 0.630 23.055

47 1.53423739 50.800 9.805 35.235 8.230 5.480 76.185 1.430 59.040

48 -3.65203408 57.615 5.520 13.720 5.700 1.235 29.350 0.590 20.910

49 -4.03169950 67.755 3.245 22.775 17.040 1.835 15.255 1.080 12.220

50 2.65247679 42.085 9.840 37.850 8.350 7.570 71.140 1.005 41.105

51 -2.70201349 54.710 7.000 17.570 9.180 1.315 26.475 0.925 25.815

52 1.40001974 48.125 10.595 33.980 5.645 4.170 58.705 0.520 39.065

53 4.04786211 14.600 14.000 26.700 7.630 8.130 60.600 3.700 62.100

54 5.04859649 17.900 11.800 34.500 11.500 13.100 63.500 3.160 64.200

55 1.60968376 27.200 11.500 25.600 9.050 4.780 54.900 2.170 42.700

56 -1.40568836 52.600 14.400 8.070 3.230 4.830 15.800 1.530 13.300

57 4.21504132 28.400 15.200 24.900 8.360 11.600 54.600 3.030 43.800

58 3.32240959 20.300 11.100 30.600 12.100 6.870 63.100 3.790 65.800

59 3.71858867 25.400 12.000 23.200 9.410 9.080 49.700 2.060 52.400

60 -0.53707478 31.500 17.500 13.600 17.500 5.730 19.400 2.350 12.400

61 4.56145967 14.700 18.900 30.000 20.000 12.400 58.700 3.030 58.000

62 -0.71339732 37.700 7.470 21.600 10.500 2.700 27.500 1.510 54.300

63 4.14040269 25.300 7.830 41.200 8.040 6.850 83.700 1.190 73.900

64 4.30805769 28.300 16.700 45.100 5.680 7.260 70.200 1.450 47.700

65 -2.16095906 45.400 16.100 6.480 3.430 3.260 12.600 1.000 9.200

66 -0.33617281 55.900 6.210 26.400 17.500 5.010 44.100 1.470 29.700

67 -3.20391349 61.400 4.100 21.400 15.500 1.460 33.800 0.990 17.000

68 0.85343990 47.200 5.660 30.200 18.500 6.800 63.400 1.520 47.900

69 0.55393957 48.200 12.400 17.900 7.520 3.990 52.900 2.200 36.600

70 0.06335840 50.100 7.080 22.200 14.200 4.950 59.500 1.070 23.700

71 0.60463330 41.900 11.900 22.200 5.660 4.860 55.800 1.260 26.500

72 1.16714606 46.800 10.900 24.100 5.860 4.200 66.300 1.520 48.000

73 0.79606100 41.600 12.900 23.000 6.090 3.800 56.500 2.160 35.900

74 2.78891097 44.300 6.690 40.900 11.000 6.350 71.900 1.470 56.200

75 1.30204649 32.600 3.810 43.200 13.500 4.170 53.800 4.810 11.100

76 0.16263889 49.700 6.950 30.000 19.500 5.040 62.200 1.410 42.000

77 0.77583644 43.400 5.480 24.900 13.100 5.660 62.700 1.320 36.100

78 1.79316087 46.300 8.690 32.700 13.300 9.290 51.800 1.280 27.100

79 2.56499828 31.600 12.300 26.100 4.030 6.460 81.700 1.230 46.300

80 -1.31512161 49.200 9.760 15.300 6.320 2.260 29.600 1.390 26.300

81 -1.07938992 53.400 10.400 14.300 4.680 1.580 43.200 1.520 37.100

82 -2.95360568 60.000 5.260 16.000 9.450 3.040 26.100 0.780 15.100

83 -0.01898106 47.600 5.920 24.300 13.400 5.750 46.100 1.120 31.900

84 -2.54568952 50.600 5.740 18.500 7.520 2.020 20.700 1.310 15.300

85 -1.08401268 53.000 5.560 21.900 12.800 4.380 41.900 1.410 25.800

CD8 Act_CD8 Div_Act_CD8 IFNy_CD4 IFNy_CD8 Treg_prop IL17A_CD4

1 14.390 11.500 49.520 4.915 21.740 93.60500 0.4150000

2 13.840 13.205 59.090 9.085 27.535 92.97000 0.3850000

3 10.020 10.915 11.535 3.045 41.360 92.84500 0.5750000

4 25.305 11.105 55.935 9.085 38.165 93.50500 0.8500000

5 17.550 9.815 12.830 2.005 19.390 91.30500 0.2500000

6 7.490 5.395 21.310 2.795 19.230 93.11000 0.2700000

7 9.065 8.900 55.690 8.455 34.310 93.93500 0.2950000

8 13.995 9.200 55.970 8.755 28.690 93.46000 0.2800000

9 8.840 8.375 45.895 12.910 46.265 93.53500 0.6100000

10 26.505 18.260 38.450 4.590 27.800 91.07500 0.3350000

11 13.900 3.785 8.985 1.690 13.755 96.36000 0.3850000

12 31.115 13.460 38.515 9.600 30.505 90.93500 0.6400000

13 18.080 3.455 8.710 1.950 13.490 93.10000 0.1650000

14 16.055 7.815 60.255 8.380 29.545 93.86500 1.0250000

15 19.235 4.930 44.240 4.355 23.725 93.51000 0.7300000

16 17.080 4.755 15.410 1.810 11.825 94.33000 0.3800000

17 28.360 5.640 6.335 1.650 16.100 93.29000 0.2700000

18 24.175 20.500 29.365 3.240 27.110 91.01500 1.0750000

19 11.410 4.455 12.675 2.580 22.560 94.87000 0.4800000

20 17.805 7.630 39.930 4.845 26.830 94.90500 0.4200000

21 33.620 13.385 33.365 1.740 16.375 92.06000 0.5150000

22 10.565 11.430 47.355 3.415 26.765 92.79500 0.5000000

23 12.140 10.910 47.475 2.620 16.955 94.73000 0.6000000

24 26.665 4.740 7.560 1.760 14.625 90.95000 0.2500000

25 35.575 19.175 31.745 3.055 19.105 72.66000 0.4600000

26 17.865 11.125 29.020 2.900 23.535 93.26000 0.7600000

27 27.880 6.615 10.910 1.110 20.565 90.52500 0.3950000

28 27.135 6.945 43.535 2.015 11.530 94.70000 1.2250000

29 18.020 2.365 6.815 1.130 8.335 95.16500 0.3850000

30 14.640 4.765 7.740 1.825 15.605 96.08000 0.5250000

31 7.365 16.415 45.830 5.475 31.365 95.40500 0.4650000

32 13.720 7.845 51.610 5.170 21.410 93.70500 1.4100000

33 20.855 3.855 10.130 1.360 8.335 91.53500 0.3500000

34 12.335 3.985 23.610 1.225 10.585 92.52500 0.6200000

35 18.260 2.810 15.015 2.010 8.880 94.67500 0.4600000

36 26.645 17.735 27.745 3.215 34.270 88.40000 0.8600000

37 7.725 6.500 18.930 3.605 22.265 94.40000 1.3150000

38 21.500 3.325 12.075 0.410 3.605 95.24500 0.3850000

39 27.155 3.900 6.500 1.020 7.420 94.96000 0.4750000

40 21.090 21.605 36.210 2.315 14.775 85.59000 2.3400000

41 18.575 8.135 46.220 2.310 17.460 96.20000 0.6000000

42 10.375 6.720 17.135 2.780 18.350 95.23500 0.7100000

43 29.465 4.220 16.695 0.830 9.825 90.60000 0.7450000

44 24.340 7.410 46.670 1.325 4.500 92.95500 0.8500000

45 28.860 7.460 43.700 1.455 7.310 90.59000 1.6050000

46 33.330 8.910 6.915 0.400 5.010 92.62000 0.4300000

47 34.585 15.035 35.205 1.540 9.265 90.04500 1.4050000

48 32.530 7.580 9.960 0.430 4.315 94.32000 0.3350000

49 14.995 5.160 6.565 1.070 9.730 96.64500 0.1800000

50 30.975 27.805 38.400 3.785 12.825 89.99500 1.4300000

51 26.945 9.665 10.630 0.665 7.805 92.89500 0.5450000

52 28.025 12.985 49.650 2.520 14.905 89.26500 2.0400000

53 6.580 11.400 24.400 5.820 38.300 90.50758 1.0426441

54 9.980 16.200 22.800 8.400 37.700 90.61363 1.0875071

55 8.930 12.400 30.600 2.850 23.700 91.14051 0.8781702

56 15.400 13.700 6.070 3.050 21.500 92.09561 0.6640916

57 7.770 19.500 26.500 9.340 36.900 90.69359 1.0397215

58 5.870 12.800 16.600 3.910 28.900 91.05230 0.9716460

59 6.140 20.500 22.500 9.130 50.100 91.79981 0.9457349

60 14.700 3.370 11.800 6.590 7.210 93.26599 0.6362830

61 7.300 10.200 23.500 9.700 21.500 91.17478 1.0277385

62 10.500 7.520 7.410 2.720 20.300 93.27272 0.6268053

63 7.560 26.500 42.500 3.110 22.000 88.81618 1.1520976

64 5.790 25.900 62.700 2.700 14.500 87.41996 1.2471387

65 11.500 7.370 8.520 1.110 20.100 92.49025 0.6008500

66 14.400 14.400 29.100 2.870 23.500 93.75014 0.6166391

67 16.400 4.370 11.600 0.340 16.200 95.17693 0.3809497

68 15.600 11.200 38.900 2.840 18.200 92.89333 0.7308085

69 26.100 14.000 38.100 1.670 21.100 90.24852 0.8791302

70 13.800 11.200 43.000 2.240 35.600 93.49048 0.6532436

71 27.900 13.300 54.100 1.850 12.200 89.52706 0.9263936

72 28.400 13.900 41.800 2.600 23.500 89.67448 0.9461751

73 25.200 10.700 44.300 2.050 15.100 89.77365 0.9209664

74 13.900 20.900 52.200 2.920 31.400 90.60534 0.9718971

75 14.400 12.100 28.800 1.960 25.100 92.39949 0.7844306

76 14.800 10.900 41.400 1.060 10.300 92.73381 0.7051832

77 14.500 18.600 40.200 1.890 16.700 91.81908 0.7933162

78 13.900 19.400 32.900 6.730 28.300 92.21576 0.8210417

79 23.800 20.900 48.600 2.360 8.070 87.62885 1.1446338

80 23.300 14.100 15.500 1.540 18.200 91.63115 0.6974956

81 24.500 13.400 16.200 1.320 16.900 91.00440 0.7484055

82 17.500 8.380 7.740 1.700 14.500 94.20965 0.4536871

83 19.100 11.500 27.000 3.970 32.000 93.35634 0.6573152

84 11.700 7.110 7.550 0.750 24.400 94.29099 0.4696145

85 15.000 7.470 33.400 2.130 22.100 93.89512 0.5692334



Caution: When imputing data, the percentages of inertia associated with the first dimensions will be overestimated.

Another problem: the imputed data are, when the pca is performed considered like real observations.
But they are estimations!! 

Visualizing uncertainty due to issing data:

--> mulrimple imputation: generate several plausible values for each missing data point

We here visualize the variability, that is uncertainty on the plane defined by two pca axes. 
<img src="Gene_expression_total_files/figure-html/unnamed-chunk-48-1.png" width="672" /><img src="Gene_expression_total_files/figure-html/unnamed-chunk-48-2.png" width="672" /><img src="Gene_expression_total_files/figure-html/unnamed-chunk-48-3.png" width="672" /><img src="Gene_expression_total_files/figure-html/unnamed-chunk-48-4.png" width="672" />

$PlotIndProc


<img src="Gene_expression_total_files/figure-html/unnamed-chunk-48-5.png" width="672" />

$PlotDim


<img src="Gene_expression_total_files/figure-html/unnamed-chunk-48-6.png" width="672" />

$PlotIndSupp


<img src="Gene_expression_total_files/figure-html/unnamed-chunk-48-7.png" width="672" />

$PlotVar


<img src="Gene_expression_total_files/figure-html/unnamed-chunk-48-8.png" width="672" />
Individuals lying on the axis have no missing data, but individuals that far away have many missing data. 
big ellipse = big uncertainty
tight elipse (line) = low uncertainty

Variable representation: 
Poins tight together )look like one) - have no missing variables --> low uncertainty
Points spread -- > higher variability -- > higher uncertainty


High uncertainty--> we should interpret the result with care

The individuals with many missing data values make the axes move, 
and thus the positions of all individuals

Therefore in the last plots every individual is getting an eclipse as they are as well influenced by the missing data of the others. 


THe plot with the dimensions shows the projections of the pca dimensions of each imputed table on the pca plane obtained using the original imputed data table

As all of the arrows are close to either the first or second axes, 
this means that the axes are stable with respect to the set of imputed tables --> we don't have evidence of instability here.



<img src="Gene_expression_total_files/figure-html/unnamed-chunk-50-1.png" width="672" />
<img src="Gene_expression_total_files/figure-html/unnamed-chunk-51-1.png" width="672" />

The function fviz_contrib() [factoextra package] can be used to draw a bar plot of variable contributions. If your data contains many variables, you can decide to show only the top contributing variables. The R code below shows the top 10 variables contributing to the principal components:
<img src="Gene_expression_total_files/figure-html/unnamed-chunk-52-1.png" width="672" /><img src="Gene_expression_total_files/figure-html/unnamed-chunk-52-2.png" width="672" />
The total contribution to PC1 and PC2 is obtained with the following R code:
<img src="Gene_expression_total_files/figure-html/unnamed-chunk-53-1.png" width="672" />
The red dashed line on the graph above indicates the expected average contribution. If the contribution of the variables were uniform, the expected value would be 1/length(variables) = 1/10 = 10%. For a given component, a variable with a contribution larger than this cutoff could be considered as important in contributing to the component.

Note that, the total contribution of a given variable, on explaining the variations retained by two principal components, say PC1 and PC2, is calculated as contrib = [(C1 * Eig1) + (C2 * Eig2)]/(Eig1 + Eig2), where

C1 and C2 are the contributions of the variable on PC1 and PC2, respectively
Eig1 and Eig2 are the eigenvalues of PC1 and PC2, respectively. Recall that eigenvalues measure the amount of variation retained by each PC.
In this case, the expected average contribution (cutoff) is calculated as follow: As mentioned above, if the contributions of the 10 variables were uniform, the expected average contribution on a given PC would be 1/10 = 10%. The expected average contribution of a variable for PC1 and PC2 is : [(10* Eig1) + (10 * Eig2)]/(Eig1 + Eig2)


<img src="Gene_expression_total_files/figure-html/unnamed-chunk-54-1.png" width="672" />
To visualize the contribution of individuals to the first two principal components, type this:

<img src="Gene_expression_total_files/figure-html/unnamed-chunk-55-1.png" width="672" />
<img src="Gene_expression_total_files/figure-html/unnamed-chunk-56-1.png" width="672" /><img src="Gene_expression_total_files/figure-html/unnamed-chunk-56-2.png" width="672" />

PCA + Biplot combination
<img src="Gene_expression_total_files/figure-html/unnamed-chunk-57-1.png" width="672" />
 In the following example, we want to color both individuals and variables by groups. The trick is to use pointshape = 21 for individual points. This particular point shape can be filled by a color using the argument fill.ind. The border line color of individual points is set to “black” using col.ind. To color variable by groups, the argument col.var will be used.

To customize individuals and variable colors, we use the helper functions fill_palette() and color_palette() [in ggpubr package].

<img src="Gene_expression_total_files/figure-html/unnamed-chunk-58-1.png" width="672" />
<img src="Gene_expression_total_files/figure-html/unnamed-chunk-59-1.png" width="672" />

Call:

lm(formula = max_WL ~ pc1 + pc2 + Parasite_challenge, data = f)

Residuals:

Min 1Q Median 3Q Max

-16.5153 -2.8123 0.0956 3.6260 10.0898

Coefficients:

Estimate Std. Error t value Pr(>|t|)

(Intercept) 81.03793 1.85414 43.706 < 2e-16 ***

pc1 0.92903 0.38685 2.402 0.0186 *

pc2 -0.04972 0.34111 -0.146 0.8845

Parasite_challengeEimeria ferrisi 11.00136 1.80891 6.082 3.86e-08 ***

Parasite_challengeuninfected 17.01763 2.64360 6.437 8.36e-09 ***

—

Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

Residual standard error: 4.999 on 80 degrees of freedom

Multiple R-squared: 0.421, Adjusted R-squared: 0.3921

F-statistic: 14.54 on 4 and 80 DF, p-value: 5.732e-09

[1] 521.6388

Call:

lm(formula = max_WL ~ pc1 + pc2, data = f)

Residuals:

Min 1Q Median 3Q Max

-17.574 -3.088 1.439 4.302 9.075

Coefficients:

Estimate Std. Error t value Pr(>|t|)

(Intercept) 92.96344 0.67013 138.724 < 2e-16 ***

pc1 -0.79584 0.27372 -2.907 0.00469 **

pc2 -0.03366 0.41754 -0.081 0.93594

—

Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

Residual standard error: 6.178 on 82 degrees of freedom

Multiple R-squared: 0.09352, Adjusted R-squared: 0.07141

F-statistic: 4.23 on 2 and 82 DF, p-value: 0.01785

df AIC

weight_lm 6 521.6388

weight_lm_exp_only 4 555.7427






################## FACS - Genes Imputation and pca ############################


# 1. Start by combining the data sets 

```r
## Adding prefixes to the columns of each data frame and joining

#Adding the suffix G to the genes
colnames(gene_expr) <- paste("G_", colnames(gene_expr), sep = "")

gene_expr <- gene_expr %>% rename(EH_ID = G_EH_ID,
                                  primary_infection = G_primary_infection,
                                  challenge_infection = G_challenge_infection,
                                  infection_history = G_infection_history,
                                  mouse_strain = G_mouse_strain,
                                  max_WL = G_max_WL,
                                  delta = G_delta, 
                                  Parasite_challenge =  G_Parasite_challenge)

#Adding the suffix f to the facs data
colnames(facs_data) <- paste("F_", colnames(facs_data), sep = "")
facs_data <- facs_data %>% rename(EH_ID = F_EH_ID,
                                  primary_infection = F_primary_infection,
                                  challenge_infection = F_challenge_infection,
                                  infection_history = F_infection_history,
                                  mouse_strain = F_mouse_strain,
                                  max_WL = F_max_WL,
                                  Parasite_challenge =  F_Parasite_challenge)

immune_data <- gene_expr %>% full_join(facs_data, by = intersect(colnames(gene_expr), colnames(facs_data)))

immune_data <- unique(immune_data)

## Warning in PCA(immune): Missing values are imputed by the mean of the variable:
## you should use the imputePCA function of the missMDA package

#let's do a pca while removing ALL NA values
res.NA.remove <- PCA(immune %>% na.omit())

# These are rather unsophisticated ways to solve the problem

##     G_CXCR3_bio   G_IL.6  G_IL.10  G_IL.13  G_IL1RN  G_CASP1  G_CXCL9   G_IDO1
## 1      20.92666 21.09045 21.78837 18.65127 16.42338 22.02920 13.60226 13.68551
## 22     21.62075 25.32600 22.92255 19.07832 20.13510 24.25054 14.53048 12.34782
## 43     23.66537 24.18021 24.90025 20.97901 18.14916 22.55511 18.99093 15.90241
## 64     20.21312 23.90781 22.31029 17.45659 16.79377 27.50341 14.03929 12.78334
## 85     23.02829 23.19571 27.67319 20.85165 18.98532 25.45624 19.20542 18.25427
## 106    23.18574 22.59572 25.82543 21.77400 19.45825 23.14097 19.07817 18.48888
##       G_IRGM1    G_MPO    G_MUC2 G_MUC5AC  G_MYD88   G_NCR1   G_PRF1  G_RETNLB
## 1   11.625516 23.16109 11.394231 12.36831 16.85699 23.33234 27.53290 11.389996
## 22  10.033986 26.67972  9.724516 14.59914 18.01044 22.89312 26.26383  7.857130
## 43   7.810604 23.03681  7.749293 12.87121 20.05994 23.96486 26.67386  9.184355
## 64  10.157602 27.67628  7.183272 14.04150 15.61895 23.45405 23.24062  3.920192
## 85   9.241544 22.25922  9.869590 14.37152 17.53845 24.12714 27.09015  8.711133
## 106  9.197374 24.94612  8.225922 11.58353 20.05389 25.43377 27.84301 15.803676
##       G_SOCS1 G_TICAM1    G_TNF  F_CD4 F_Treg F_Div_Treg F_Treg17  F_Th1
## 1   13.025961 19.82281 21.01065 44.900  6.385     16.205   13.520  6.780
## 22  10.292493 17.66099 22.36282 46.145  7.005     21.365   11.565 10.920
## 43   9.205008 19.11736 22.81213 56.220  7.150     12.455    9.505  2.965
## 64  10.692568 15.46167 18.96024 40.590  6.450     23.760   12.780  9.250
## 85  10.586118 17.03506 24.77639 52.245  8.695     13.465   14.400  2.545
## 106 10.037031 18.92915 25.01909 46.895  6.890     13.355    7.035  2.900
##     F_Div_Th1 F_Th17 F_Div_Th17  F_CD8 F_Act_CD8 F_Div_Act_CD8 F_IFNy_CD4
## 1      71.200  0.890     46.875 14.390    11.500        49.520      4.915
## 22     75.115  1.075     42.390 13.840    13.205        59.090      9.085
## 43     19.840  1.630     30.055 10.020    10.915        11.535      3.045
## 64     81.210  1.705     78.305 25.305    11.105        55.935      9.085
## 85     27.850  1.060     27.445 17.550     9.815        12.830      2.005
## 106    25.520  0.695     32.195  7.490     5.395        21.310      2.795
##     F_IFNy_CD8
## 1       21.740
## 22      27.535
## 43      41.360
## 64      38.165
## 85      19.390
## 106     19.230

## $Dim.1
## $quanti
##               correlation      p.value
## F_Th1           0.7650588 1.703309e-24
## F_Th17          0.7598164 5.340582e-24
## F_IFNy_CD8      0.7407478 2.697419e-22
## F_IFNy_CD4      0.7316087 1.568174e-21
## G_IL.13         0.7122022 5.241535e-20
## G_IL1RN         0.7084149 1.005338e-19
## G_IDO1          0.6947200 9.739906e-19
## G_TNF           0.6888737 2.471676e-18
## F_Div_Th17      0.6506015 6.644992e-16
## G_MPO           0.6252548 1.773772e-14
## G_RETNLB        0.5982680 4.280621e-13
## F_Act_CD8       0.5668512 1.220574e-11
## F_Treg          0.5515657 5.504312e-11
## G_IL.10         0.5408752 1.509767e-10
## F_Div_Th1       0.5257661 5.923963e-10
## G_CXCR3_bio     0.4899884 1.168833e-08
## G_MYD88         0.4702646 5.255444e-08
## G_TICAM1        0.4701173 5.312920e-08
## G_PRF1          0.4668096 6.773196e-08
## F_Div_Treg      0.4444984 3.264315e-07
## F_Div_Act_CD8   0.4312808 7.871126e-07
## G_IL.6          0.4176539 1.877346e-06
## G_NCR1          0.4097123 3.062324e-06
## G_MUC5AC        0.3929269 8.273247e-06
## G_CXCL9         0.3664398 3.569232e-05
## G_CASP1         0.3418565 1.242051e-04
## F_Treg17        0.2465892 6.399767e-03
## G_IRGM1        -0.5211005 8.914897e-10
## G_SOCS1        -0.5833882 2.188131e-12
## F_CD8          -0.6968069 6.947760e-19
## F_CD4          -0.8437915 5.900136e-34
## 
## attr(,"class")
## [1] "condes" "list"  
## 
## $Dim.2
## $quanti
##               correlation      p.value
## G_CXCL9         0.7283817 2.870119e-21
## G_IL.13         0.6706403 3.939175e-17
## G_IL.10         0.6441883 1.570495e-15
## G_PRF1          0.6185593 4.020897e-14
## G_TICAM1        0.6112244 9.640520e-14
## G_NCR1          0.6098601 1.131526e-13
## G_TNF           0.5964205 5.265852e-13
## G_RETNLB        0.5650170 1.468355e-11
## G_IDO1          0.5014129 4.680602e-09
## G_CXCR3_bio     0.4718764 4.664334e-08
## G_MYD88         0.4597753 1.125655e-07
## G_IL.6          0.4491703 2.370129e-07
## G_IRGM1         0.4219901 1.429567e-06
## F_CD4           0.3055332 6.547726e-04
## G_SOCS1         0.2532034 5.076460e-03
## F_Treg         -0.2380665 8.551869e-03
## F_IFNy_CD8     -0.2575118 4.351615e-03
## F_CD8          -0.3127221 4.789682e-04
## G_MPO          -0.3673520 3.401058e-05
## F_IFNy_CD4     -0.3945495 7.533015e-06
## F_Th1          -0.5348894 2.615960e-10
## F_Div_Th17     -0.5654332 1.408179e-11
## F_Act_CD8      -0.6210470 2.973398e-14
## F_Div_Treg     -0.6398160 2.790472e-15
## F_Div_Act_CD8  -0.7326830 1.279869e-21
## F_Div_Th1      -0.7673426 1.025732e-24
## 
## attr(,"class")
## [1] "condes" "list"  
## 
## $Dim.3
## $quanti
##             correlation      p.value
## G_MUC2        0.7709011 4.600148e-25
## G_CASP1       0.7312700 1.671562e-21
## G_MUC5AC      0.6001274 3.470433e-13
## G_SOCS1       0.5513289 5.630884e-11
## G_IRGM1       0.4754587 3.569935e-08
## G_CXCR3_bio   0.3000311 8.274102e-04
## G_MYD88       0.2150116 1.786775e-02
## G_MPO         0.1995167 2.823459e-02
## G_CXCL9      -0.2240811 1.348067e-02
## G_TICAM1     -0.2308450 1.085158e-02
## 
## attr(,"class")
## [1] "condes" "list"  
## 
## $call
## $call$num.var
## [1] 1
## 
## $call$proba
## [1] 0.05
## 
## $call$weights
##   [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1
## 
## $call$X
##            Dim.1 G_CXCR3_bio   G_IL.6  G_IL.10  G_IL.13  G_IL1RN  G_CASP1
## 1     0.38393739    20.92666 21.09045 21.78837 18.65127 16.42338 22.02920
## 22    2.63643667    21.62075 25.32600 22.92255 19.07832 20.13510 24.25054
## 43    2.00034162    23.66537 24.18021 24.90025 20.97901 18.14916 22.55511
## 64    2.01438301    20.21312 23.90781 22.31029 17.45659 16.79377 27.50341
## 85    1.26179437    23.02829 23.19571 27.67319 20.85165 18.98532 25.45624
## 106   2.06168075    23.18574 22.59572 25.82543 21.77400 19.45825 23.14097
## 127   2.38696055    20.19632 23.91450 21.62915 18.76407 18.55582 23.11127
## 148   2.78216918    23.73105 21.14346 23.61026 19.84489 17.67666 25.06357
## 169   4.50400857    23.18462 22.02135 24.37189 20.88345 19.31653 22.45011
## 190  -0.27703529    19.21698 19.62519 22.94861 16.18605 17.29027 23.55407
## 211   0.87599108    22.52077 23.65471 23.85751 20.73572 20.58918 23.81598
## 232   0.44771425    19.12177 25.26331 20.62422 16.14895 17.38254 22.70095
## 253  -0.70630212    22.45032 22.69335 23.61657 19.44235 17.74972 22.49518
## 274   0.20902462    22.51152 20.95665 23.01596 18.99110 16.69397 22.84267
## 295  -0.30595182    21.26747 21.85538 23.74287 19.62806 19.67336 22.04784
## 316  -3.29612066    18.00746 18.38914 18.74826 16.15896 15.10803 23.38067
## 337   1.92481446    19.10121 20.98553 22.42743 17.74906 16.44270 23.42149
## 358   0.89430406    22.41295 20.25383 23.03015 20.86906 17.63167 22.85872
## 379  -0.02297122    21.44775 18.45020 27.89777 18.26056 17.32784 23.05298
## 400  -0.90422295    21.07130 20.03526 21.08242 16.86255 17.43922 22.51961
## 421   0.54713244    20.92691 21.09167 24.07971 18.20536 17.14403 22.76425
## 442  -2.97562943    19.36417 15.07182 20.66591 16.07438 16.54046 24.16598
## 463   0.27535175    18.14328 17.39709 21.31080 15.86963 12.63218 23.57993
## 484  -0.14036040    20.03795 28.89199 21.07542 18.05607 18.66006 22.98125
## 505  -0.42429273    23.44335 23.78059 24.23830 20.46744 16.75777 23.76993
## 526  -0.71666176    20.31368 19.93494 21.62254 16.87035 17.64324 22.86814
## 547   0.86995217    24.81556 21.68421 25.12770 21.64005 18.20610 22.86023
## 568  -1.58024050    20.53629 25.89126 23.68561 19.78150 16.66748 28.45142
## 589   1.40301639    20.53802 20.02823 20.76649 18.60375 17.42489 22.69451
## 610  -1.32821772    18.97607 19.07150 21.15728 15.69609 13.40232 24.16373
## 631   0.04307676    23.27692 19.71861 23.52992 19.80666 19.58139 21.79801
## 652  -0.89863421    21.66393 20.33407 20.44784 19.45649 17.72250 23.41526
## 673   0.26367230    23.07634 22.39279 23.69841 19.64607 19.91583 23.28426
## 694   0.89455474    22.53733 18.60527 22.20044 18.56122 17.97018 25.19149
## 715   1.36682346    22.10287 20.06929 23.90926 20.00712 19.46909 21.63034
## 736  -0.39813847    24.60278 18.46774 26.19512 19.99267 19.76563 23.74273
## 757  -4.48215599    18.58505 19.05836 17.86940 15.96475 14.97143 20.93711
## 778  -1.35581435    16.86408 13.21835 17.79117 13.30510 14.25306 24.43256
## 799   0.74871763    22.15447 25.41724 22.04275 18.07179 17.18358 29.98603
## 820  -0.53626902    21.16552 18.10097 22.65766 18.78266 17.71976 24.28977
## 841  -1.09130055    24.69742 19.05980 23.16757 19.00740 18.98606 22.78772
## 862   0.33945821    21.32868 21.65526 23.07222 18.96714 17.83004 22.53197
## 883  -3.10404161    18.70600 17.79219 18.49236 14.44634 14.68719 24.20202
## 904  -1.26449650    22.12217 21.87536 23.32834 19.35099 18.26465 23.79661
## 925  -2.08908093    19.16785 17.97426 19.89393 14.76001 15.60916 23.59150
## 946  -3.96131425    18.19756 14.40759 20.83681 15.31739 17.24873 25.16027
## 967  -2.21909310    21.64540 17.73381 20.98086 17.60556 17.40872 20.43007
## 988  -1.42807826    20.18662 16.57967 19.05747 16.36073 16.32045 22.82450
## 989  -1.46939246    18.21390 29.81903 18.33959 15.01577 13.28305 25.93133
## 1010 -2.10884520    20.61515 23.03621 29.98877 20.24780 12.75221 20.43024
## 1011 -0.95106875    21.00294 23.67766 24.20689 20.77857 15.08824 20.90917
## 1012 -4.37971101    20.65725 21.54047 23.25961 18.17757 11.65156 22.92319
## 1013 -0.89023691    21.31698 26.71171 25.52867 21.11842 13.83283 21.43852
## 1014 -4.73594992    20.20715 24.37765 22.34004 18.79052 11.97324 21.03117
## 1015 -4.27283734    18.40655 21.07280 21.44767 16.18907 11.82725 21.70017
## 1016 -3.29612865    20.89049 23.48067 22.36207 18.97398 11.85427 21.44578
## 1017 -2.44217906    21.14188 22.70663 23.60720 19.39390 13.10425 25.34167
## 1018  0.77833863    21.95768 22.34438 26.11948 20.66828 15.37061 21.28205
## 1019 -4.36799169    18.21933 25.24031 20.51353 16.96765 12.28923 22.26009
## 1020 -3.27490287    24.65157 23.53264 23.43752 19.89853 11.43627 25.73564
## 1021 -2.73705403    23.52242 27.13311 24.22711 19.87466 11.27634 21.92730
## 1022  7.72587673    22.91576 25.39795 28.87344 24.53315 16.75650 22.16068
## 1043  8.32409310    25.66519 26.35204 27.17831 24.41435 19.70521 26.08998
## 1064  6.99688301    24.22064 26.80839 28.05381 24.85629 19.23186 27.75083
## 1085  2.79282594    22.86653 23.56129 25.01750 22.76053 18.03112 21.69953
## 1106  6.21696744    22.76071 25.75475 27.95595 23.56784 15.84708 23.58463
## 1127  8.68538312    23.52785 29.95433 28.18004 25.14229 23.13962 24.40739
## 1148  7.73408669    22.75096 26.15021 25.69999 24.13682 17.58811 23.01611
## 1169  3.54490029    21.48844 22.60560 29.13131 22.81402 18.63039 20.75492
## 1190  8.92920843    25.80037 29.77186 29.09962 26.19730 20.02498 26.91510
## 1211  3.03849241    21.03955 26.23716 26.44454 21.78755 16.61041 22.30920
## 1232  6.58125165    21.52073 27.46334 24.91564 22.51335 17.10527 24.02626
## 1253  6.57389787    22.66652 18.92939 25.56209 22.48327 18.86001 24.83386
## 1274 -3.01546346    21.81679 20.56615 26.30993 18.96533 12.25711 21.65407
## 1275  4.31479824    22.67323 26.18491 27.22396 24.69858 20.08638 25.34344
## 1296  2.34693984    23.46890 22.68031 23.16663 22.41758 18.06204 22.36512
## 1297 -4.55987557    19.91255 22.64689 22.62087 17.89643 11.27495 21.54675
## 1298 -4.39196613    20.08603 21.31040 22.15091 17.60702 10.39335 20.70334
## 1299  8.47098790    24.06576 28.75247 23.54490 22.87320 21.42000 27.75544
## 1300 -3.67765924    21.27995 18.29274 24.38039 18.24103 12.06338 20.29093
## 1301 -4.27505546    20.22281 24.09667 19.95704 17.03426 12.45559 21.84692
## 1302 -3.80723774    24.54876 24.16064 25.98836 21.00168 14.72332 24.56166
## 1303 -3.08238850    19.66524 28.21305 25.28405 20.88149 12.24016 21.28489
## 1304 -1.16370423    21.37939 22.23118 24.59390 20.27709 17.24273 21.67512
## 1305 -2.59057394    20.76001 22.04766 22.25595 17.90817 15.60735 20.92126
## 1306 -2.88781460    18.55728 25.20569 24.22079 18.44548 15.59951 19.99790
## 1307  5.08656521    20.76581 23.12287 24.20175 24.65534 16.35892 21.11699
## 1308 -3.78660664    19.80003 22.51022 24.68163 18.29073 12.63565 23.09224
## 1309 -3.90317899    20.95861 22.68883 26.12950 20.04791 11.69210 22.80543
## 1310 -3.94140571    21.59717 26.58406 21.70019 21.35713 11.07716 20.93479
## 1311 -3.83609061    18.34869 21.28534 21.67449 15.43664 14.73684 19.96002
## 1312 -1.73286023    21.69704 25.01886 23.22688 17.92247 15.92684 21.19813
## 1313  0.33646080    21.45424 19.31163 23.59355 18.98706 18.17187 22.41502
## 1314 -5.47990797    19.13856 16.59937 17.90145 14.23954 12.16899 21.02919
## 1315  0.88873573    20.68170 26.84019 24.26723 19.69519 17.50911 20.48537
## 1334 -0.62043459    18.54700 23.16907 23.37539 20.64206 17.68361 21.37431
## 1353  0.81885489    20.46746 18.96176 24.76140 17.74507 17.02771 20.42448
## 1372  0.87465675    19.20522 21.60006 22.75786 19.52374 19.51251 20.75696
## 1391 -4.02784785    21.53098 18.24249 22.16032 17.34487 13.69251 24.61451
## 1392  2.03632707    20.54109 26.92355 21.56791 22.61048 20.05260 24.82729
## 1411 -0.78981833    20.32615 23.60529 21.83610 16.52889 15.60828 20.62177
## 1430 -0.58085630    20.58743 22.64934 21.05282 15.64012 15.78011 21.04427
## 1449 -0.52696669    21.30977 24.81393 20.53981 15.96387 15.62994 22.11439
## 1468  1.78942024    24.78347 20.20337 23.52439 19.78487 14.97696 22.93029
## 1487  3.50023687    22.77867 25.05195 25.39121 23.10120 20.21300 25.57550
## 1506 -1.17400431    20.05828 17.73281 19.67574 14.96183 14.91150 20.39827
## 1525  0.06090527    20.06144 19.48198 22.33842 15.84020 15.91221 20.98834
## 1544 -4.80499397    21.33039 19.22344 21.16372 16.83008 12.50428 22.24713
## 1545 -0.83680192    21.08006 18.49392 19.74151 15.73607 12.83904 21.46682
## 1564  0.65298618    18.95654 21.82732 26.77299 21.41995 19.69194 20.78469
## 1583  1.32430798    20.24860 28.76398 23.93211 21.10862 20.95269 21.23007
## 1602 -0.24747765    19.01495 26.92797 23.32124 20.67509 18.21358 20.67898
## 1621  0.39545401    22.14929 22.09470 24.41267 16.81424 17.08754 20.84055
## 1640 -4.62314088    23.41073 17.46569 19.79551 14.49658 13.13472 25.08039
## 1641 -0.32658386    18.84797 25.09494 24.87735 20.31023 15.62935 20.16156
## 1660  0.86829918    21.05519 27.11805 23.21401 20.25871 18.40590 21.07134
## 1679  3.09061869    21.52990 22.73974 23.55340 19.81853 18.21812 23.60838
## 1700  1.98977099    21.62663 22.96523 23.84156 20.00586 17.53337 23.29520
## 1721 -0.97944106    19.21490 18.83110 19.56207 14.95676 16.12013 23.15203
## 1742 -2.35460526    20.56672 21.29702 22.25107 17.74903 15.06501 22.43768
## 1763  0.55363583    20.87223 21.69687 22.55136 18.42015 16.78295 23.11778
##       G_CXCL9    G_IDO1   G_IRGM1    G_MPO    G_MUC2  G_MUC5AC   G_MYD88
## 1    13.60226 13.685507 11.625516 23.16109 11.394231 12.368312 16.856985
## 22   14.53048 12.347823 10.033986 26.67972  9.724516 14.599135 18.010443
## 43   18.99093 15.902410  7.810604 23.03681  7.749293 12.871210 20.059938
## 64   14.03929 12.783337 10.157602 27.67628  7.183272 14.041496 15.618948
## 85   19.20542 18.254268  9.241544 22.25922  9.869590 14.371520 17.538455
## 106  19.07817 18.488880  9.197374 24.94612  8.225922 11.583533 20.053889
## 127  14.67773 14.430931  8.600942 24.90775  8.730690 11.900492 18.177256
## 148  14.21946 15.666291  8.297135 25.61896  7.522414 13.148207 19.038180
## 169  16.20309 14.952342  8.997360 29.21133  8.156661  8.684992 20.392755
## 190  12.88829 11.663551  9.052160 27.46451  8.642571 10.342714 14.618691
## 211  20.30617 16.930006  8.162201 25.54124  8.859693 15.460500 19.281729
## 232  13.01806 10.705361  7.565302 24.91439  6.904949 15.359870 14.612337
## 253  17.39209 15.675249  9.841508 25.19862  7.871219  8.678551 16.285136
## 274  15.54217 13.079090 10.548003 23.12428  9.808142 10.449504 16.981842
## 295  19.29231 18.558979  9.218357 28.14862  8.669347 10.198480 16.960683
## 316  17.12064 15.731242  9.193427 23.38627  8.394537 10.196126 14.609839
## 337  13.68531 12.914861  9.322633 21.87048  8.714876 12.295662 15.888646
## 358  18.63569 19.069602  9.447187 27.92150  8.040773  9.121950 17.378285
## 379  12.95849 14.508282  9.598510 27.07087  7.807939 10.415893 15.854892
## 400  14.61636 13.138920  8.335187 24.66545  7.790361  9.038129 16.616529
## 421  16.18053 13.646650  9.565223 25.42206  8.771323  9.468288 15.065539
## 442  15.03703 16.004009  8.438642 27.97673  8.473955 10.951688 12.671592
## 463  14.00879 11.186614  9.439790 25.15912  8.613752 20.293679 13.916375
## 484  14.61884 12.098614  8.954314 24.31267 17.990707 24.237810 15.120134
## 505  21.33524 18.017771 11.480787 19.99031 10.255215 10.923709 17.310957
## 526  16.94201 12.246575  8.748695 26.20443  7.940369 12.292991 15.518893
## 547  18.99404 18.253549 10.577026 21.14607  8.761090  9.023115 19.547397
## 568  16.56531 16.273956 13.691213 19.74174 12.038068 20.929919 15.678849
## 589  15.85200 12.968113  8.203141 24.26771  8.233775 17.425917 17.354687
## 610  13.93241 11.229936  9.725386 24.64733  6.814177 11.003653 13.408224
## 631  18.71677 17.629490 10.881357 25.72409 10.267396 11.219287 15.989496
## 652  19.96504 19.114217 11.739965 21.94526 12.198908 12.960735 16.747558
## 673  18.19233 16.734890  8.942380 24.38990  8.418066  9.847442 16.538393
## 694  18.54367 17.279974  9.036738 22.05586  7.435172 13.026381 16.057834
## 715  17.56039 18.416046  8.848435 25.72081  8.204233  9.882749 18.079438
## 736  18.40169 19.753084  9.752966 28.66910  9.577180 10.404196 17.139011
## 757  16.51682 16.471883 11.495214 20.75777 10.413618 11.989536 14.774482
## 778  13.82651  8.661838  7.154126 23.86028  7.951477 15.116064 10.230339
## 799  13.24525 13.915862  8.987010 27.49619  9.597302 21.741745 16.270490
## 820  17.31654 17.871126 10.481867 23.27109  9.326657  9.908069 15.881726
## 841  18.82635 17.110750 10.481346 24.32100  8.573053  9.253118 16.642453
## 862  17.26648 15.929076  8.958850 22.70039  7.632720  8.440455 19.807910
## 883  12.21244  9.754557  9.836893 23.37686  9.851718 11.585622 14.407068
## 904  20.24753 19.708596  9.977461 24.92838  8.415812  8.076470 17.337172
## 925  13.56512  9.842353  9.029007 24.40325  9.316026  9.889951 13.850419
## 946  16.38290 15.927925  8.049613 20.55254  7.563250 12.350998 11.128010
## 967  16.55790 15.251946  9.385581 27.61566  8.563067  9.972695 15.725963
## 988  17.97308 18.265865  9.486106 23.34994  7.878306 10.787435 12.493822
## 989  11.98283 10.025161  8.136754 27.16886 10.834516 29.918079 13.929742
## 1010 18.32872 16.717158 10.030781 16.37685  8.541946  8.852514 20.404963
## 1011 21.96734 18.171699  9.531294 15.92918  7.957801  8.211709 24.785884
## 1012 16.90758 12.292333 11.168791 17.07884  8.345124 10.313463 15.319679
## 1013 22.16466 18.373123  9.563630 16.38449  8.132526  8.572920 23.240718
## 1014 16.66383 11.993194 11.576390 17.15236 10.280913 10.532018 18.139879
## 1015 15.42738 12.523361  9.928879 16.55492  8.291121  9.120236 13.839477
## 1016 16.72246 13.721075 10.479662 16.32184  8.641474  8.817069 19.929199
## 1017 18.64811 15.843818 10.788702 17.07038  9.428260  9.364003 18.078884
## 1018 22.49707 18.930756  8.050492 16.71614  7.611355  7.888725 20.995390
## 1019 15.25164 11.803676 10.108555 16.57170  9.364101  9.848285 15.544608
## 1020 20.65578 12.237259 11.398526 16.60661  9.628627  9.639826 19.190942
## 1021 18.51135 13.708155 10.256888 16.39160  9.063478  9.058345 20.478204
## 1022 23.45426 21.459525  7.149357 25.63594  6.211322 10.154484 24.906656
## 1043 19.96006 20.724537  9.016223 27.77559  9.246984 14.123916 24.948713
## 1064 23.07473 27.169505  8.986193 27.97227  8.883982 23.694956 27.782637
## 1085 24.18800 22.517576  8.747040 26.39468  7.865111  9.540464 19.913584
## 1106 23.33492 22.130637  9.505613 20.34651  9.117813 10.210623 25.644537
## 1127 22.51919 24.362430  7.028294 27.61117  9.249620 25.681823 23.705403
## 1148 24.12845 22.364820  7.679259 28.01318  7.529806 12.495365 24.056632
## 1169 22.14808 21.229097  8.823074 23.37908  7.071763  8.979468 18.841489
## 1190 23.73669 26.746953 10.566932 27.03879 10.301982 15.246147 24.071985
## 1211 20.71644 20.531902  8.015308 26.53156  6.875894 15.616582 18.824360
## 1232 18.86451 21.448918  7.324264 27.47612  6.626930 13.267206 25.219254
## 1253 16.34429 22.097978  7.796770 27.26642  8.052046 18.066238 24.484515
## 1274 13.14677 13.300336 10.259382 16.46249  8.504597  8.874519 20.875416
## 1275 23.74179 25.029717  9.255368 22.75586  8.579815 29.113148 28.078962
## 1296 19.88270 20.563533 10.008556 22.27321  8.371019  9.336553 24.674035
## 1297 18.09229 12.713460 10.729052 16.12279  9.020236  9.326122 17.670409
## 1298 18.47461 11.083212  9.706705 15.60862  8.225850  8.669181 17.396433
## 1299 18.54329 18.841289  7.212158 28.72646 11.580169 26.744894 20.536848
## 1300 14.49042 12.360175 10.292874 16.43850  8.630854  8.630570 17.291781
## 1301 14.45594 11.304476  9.925298 17.66268  9.721816 10.491249 15.854700
## 1302 22.72566 15.063209 14.224817 20.66631 12.365167 12.636119 18.984608
## 1303 23.29208 13.730573 11.186153 16.54841  9.411632  9.773155 28.008621
## 1304 19.01887 18.925460 10.323671 17.42215  8.655927  9.526401 18.953311
## 1305 14.94362 12.729950  9.898791 17.78050  8.541098  8.728032 16.724469
## 1306 18.02023 16.255302 10.261519 17.17013  8.567059  8.840712 17.923550
## 1307 21.25767 21.997595  7.290380 28.70681  6.445662  9.138264 13.951069
## 1308 20.79058 13.061516 11.602668 17.49598  9.719942 10.305993 10.797975
## 1309 21.78558 12.274416 11.183381 17.01788  9.354890  9.778690 10.666151
## 1310 22.47699 11.437480 10.668408 16.28529  8.850896  9.096841 10.200785
## 1311 15.04185 13.407077  9.601861 16.79333  7.966876  8.117302  9.107979
## 1312 18.33013 15.595453  9.425018 16.97132  7.794905  8.401166  9.336994
## 1313 14.38687 12.455074  9.436140 24.79474  8.197041  9.244237  9.956077
## 1314 10.82695  9.136530  9.375088 17.69353  8.866534  9.777502 10.271472
## 1315 18.70781 14.946433  8.839694 19.82217  7.487277  8.125193  8.844189
## 1334 24.86841 19.502043  9.457993 18.28340  8.071410  8.206934  9.252441
## 1353 15.35375 14.252057  9.304423 23.43023  7.997043  9.088218  9.269372
## 1372 18.79592 16.970430  8.739251 20.15552  9.256436 10.444694 10.814310
## 1391 14.09206 12.763815 12.235026 19.78562 11.179115 11.697763 12.527439
## 1392 18.86993 17.037959 10.412347 19.15029  9.342203  9.990472 11.061339
## 1411 13.72277 12.100585  8.635025 19.64736  7.290007  8.052774  8.988102
## 1430 14.14905 14.815813  9.722631 18.87126  8.935368 10.212263 10.256080
## 1449 14.05901 11.611228  8.832139 17.72639  7.722895  8.278575  9.471053
## 1468 16.14029 14.976568 10.745571 18.11004  9.538819 10.048293 10.673092
## 1487 21.90772 20.337306 10.781881 19.84596  9.299197  9.821379 10.803154
## 1506 12.87900 11.674964  8.890484 21.49815  8.237312  8.762589  9.752521
## 1525 11.55303 11.659147  9.496184 20.05410  7.969417  8.572635  9.429172
## 1544 15.30404 11.142649 11.492399 18.39825 10.125236 10.724162 11.321831
## 1545 13.54572 11.447434 10.481360 18.73255  9.916628 10.691091 11.307609
## 1564 25.69238 20.921377  9.314263 19.02520  8.686559  9.464939 10.506455
## 1583 25.34770 20.689122  8.418992 24.76759  7.942093  9.095062  9.772552
## 1602 25.74388 20.553644  8.419839 21.41300  6.748056  7.525599  8.790171
## 1621 15.14489 15.490731  9.344918 21.30055  7.847138  8.507111  9.580745
## 1640 12.26390  8.769283 10.113600 19.62256 10.336654 11.690665 10.798740
## 1641 20.93232 18.051913  9.677846 16.56573  7.916451  8.172702  9.524207
## 1660 17.73318 16.366598  8.701905 20.94546  7.665722  8.340444  9.444841
## 1679 17.30498 17.066943  8.655827 25.28843  8.453515 13.596362 16.840473
## 1700 18.03878 17.066228  9.101223 23.82069  8.607356 12.732305 17.097344
## 1721 12.05817 11.001204  8.676909 24.40806  8.526599 12.552244 10.998672
## 1742 16.64099 13.792882  9.982036 20.48069  8.947085 10.482400 14.447618
## 1763 16.37342 15.067583  9.146649 23.40381  8.644727 12.313631 15.194739
##        G_NCR1   G_PRF1  G_RETNLB   G_SOCS1 G_TICAM1    G_TNF    F_CD4    F_Treg
## 1    23.33234 27.53290 11.389996 13.025961 19.82281 21.01065 44.90000  6.385000
## 22   22.89312 26.26383  7.857130 10.292493 17.66099 22.36282 46.14500  7.005000
## 43   23.96486 26.67386  9.184355  9.205008 19.11736 22.81213 56.22000  7.150000
## 64   23.45405 23.24062  3.920192 10.692568 15.46167 18.96024 40.59000  6.450000
## 85   24.12714 27.09015  8.711133 10.586118 17.03506 24.77639 52.24500  8.695000
## 106  25.43377 27.84301 15.803676 10.037031 18.92915 25.01909 46.89500  6.890000
## 127  23.25482 23.54348 11.930951 10.137282 17.89026 20.40686 49.47000  6.065000
## 148  23.69673 28.00436 10.795116 10.187464 17.98634 21.91510 45.74000  6.520000
## 169  23.81112 26.24627 11.763447  9.833251 20.04689 25.99834 46.33000  6.465000
## 190  21.39968 20.45141  4.079604 11.242170 15.12650 18.21831 43.32500  8.915000
## 211  23.66060 26.62192 12.512554  8.390115 17.00279 24.39284 68.01000  3.630000
## 232  20.06957 21.01384  3.598778  8.892853 14.34632 18.18376 37.43500  9.045000
## 253  24.37670 25.10224 11.645965 10.674034 15.64940 20.93638 53.25000  6.895000
## 274  23.76296 27.17679 12.534258 11.718299 17.56715 20.51972 47.34000  6.465000
## 295  23.89841 24.64252 11.212956 10.034478 16.84957 22.49043 61.52500  5.650000
## 316  18.00615 22.71284  6.937463 10.044808 15.08446 17.05868 51.47500  6.690000
## 337  21.33841 26.20900  5.973854 10.589004 17.65482 19.35511 36.15500  8.875000
## 358  29.49340 25.66098 14.362461 10.790189 18.71333 23.10196 54.57500  5.110000
## 379  23.12706 24.63635  5.662282 10.323638 15.91257 19.63243 58.92000  5.075000
## 400  22.28476 23.09671  6.708141  9.097796 16.83769 19.32845 49.92500  7.915000
## 421  24.36829 25.16968  8.373846 10.727382 15.39051 19.76802 53.24000  5.215000
## 442  18.33988 22.09717  3.437346 10.438836 13.27494 18.82194 49.35000  9.015000
## 463  18.49862 22.59840  4.203089  9.684278 13.27406 22.08087 28.29500 27.230000
## 484  20.80061 24.78750  4.605416 10.017204 15.04402 23.69131 53.27000  6.670000
## 505  25.28210 25.69449  9.644582 12.041930 19.34746 26.10923 54.26500  9.475000
## 526  20.79229 26.11614  6.063100  9.980612 14.75829 18.44981 48.49000  5.220000
## 547  23.63638 27.39362 12.795983  9.838008 22.32244 29.87482 56.78000  4.835000
## 568  22.63025 28.71924 13.919183 15.560557 16.95622 20.43844 67.43000  3.900000
## 589  21.23689 29.16415  9.982388  9.482890 14.86344 21.08135 53.51000  4.525000
## 610  20.03371 21.10798  5.917482 11.194286 13.04953 16.84558 49.93500  6.265000
## 631  20.77055 28.50238 10.511800 11.429176 16.45653 24.00758 42.86000  8.465000
## 652  20.53246 29.53929 10.842803 12.155859 17.76277 21.50840 55.30500  7.315000
## 673  25.24495 25.38933 11.824440  9.660671 15.41369 20.83225 52.10000  5.205000
## 694  21.49756 25.56295  7.036342  8.830993 15.62009 20.40643 48.70500 11.315000
## 715  22.91124 25.93300 12.132540  9.535813 16.56348 22.45497 42.07000  5.530000
## 736  21.85548 26.16469  9.879838 11.097173 16.94929 24.08582 55.00500  4.635000
## 757  19.67674 21.14482  8.466972 12.429422 15.73371 16.46384 55.13500  4.955000
## 778  17.36359 18.08027  3.785109 10.974022 12.41300 13.78664 48.92000 14.300000
## 799  23.07639 24.78306  5.259263 10.307205 14.37050 19.70445 60.70500  3.740000
## 820  19.59789 25.15236 11.495341 11.487913 16.29785 20.60002 49.85000  4.700000
## 841  22.90715 25.42066  7.758496 11.493739 17.45863 22.01304 53.75500  9.235000
## 862  24.18657 29.51591  9.225770  9.632405 17.44476 21.63822 48.38000  6.965000
## 883  19.93898 20.38618  4.346449 11.657992 15.33566 15.86004 46.69500  9.310000
## 904  21.63473 28.11725  9.187486 10.970666 19.30253 21.39020 58.17000  7.095000
## 925  18.81508 21.15985  4.867295 11.106637 15.25927 16.16250 50.80000  9.805000
## 946  17.17266 21.54708  3.690941 10.228503 13.47368 18.09514 57.61500  5.520000
## 967  21.39350 23.41759  8.189116 10.139407 15.24493 17.65270 67.75500  3.245000
## 988  17.79122 25.89262  4.204721 10.569843 12.91764 17.50383 50.89402  7.658338
## 989  18.11990 19.92611  3.577107 10.324091 14.54200 16.01331 48.12500 10.595000
## 1010 25.36659 26.01465 10.749170 11.024760 21.19794 21.01304 62.22072  5.119414
## 1011 26.75319 27.09819  9.755923 10.478270 22.50241 21.56508 58.33307  5.882857
## 1012 24.26265 27.12899 10.841208 12.989070 16.99448 20.03119 67.63015  4.134243
## 1013 27.48604 27.18535  9.478791 10.607035 21.82549 21.98348 58.80046  5.767698
## 1014 26.70705 25.41207 12.030827 13.123553 21.09282 21.07389 70.49765  3.512972
## 1015 20.19892 21.79024  9.145387 10.833533 15.29784 19.01250 62.88455  5.218830
## 1016 25.53460 26.41866  9.278296 11.303618 21.34152 21.31616 64.63679  4.699616
## 1017 26.75811 25.29939  9.923647 11.828319 19.06670 21.78523 61.83057  5.248561
## 1018 23.38357 26.57793  8.262385  8.890513 20.80948 23.70044 50.58155  7.472741
## 1019 22.93460 25.11673  9.047844 11.867247 16.30778 18.36694 64.97428  4.746851
## 1020 23.87387 28.88317  9.635853 12.837053 19.15818 22.89580 66.53513  4.258283
## 1021 26.26590 23.85662  9.703631 11.627023 21.52452 22.65241 64.14684  4.747550
## 1022 27.00035 28.91190 18.572389  8.318661 29.57724 23.13135 14.60000 14.000000
## 1043 26.79737 28.72793 17.913556 10.084373 25.58661 27.70538 17.90000 11.800000
## 1064 25.83862 27.59474 20.897073  9.482244 24.73153 27.59754 27.20000 11.500000
## 1085 29.07498 28.08288 16.332964  9.665532 21.42768 23.91542 52.60000 14.400000
## 1106 26.89351 28.29766  9.493187 11.330597 26.12898 26.67485 28.40000 15.200000
## 1127 27.39339 29.29848 14.714598  8.041715 24.10520 28.93255 20.30000 11.100000
## 1148 25.88560 25.43324 20.720319  7.087203 26.26861 27.69078 25.40000 12.000000
## 1169 26.76586 28.02451 10.975465  9.162248 19.60484 29.13404 31.50000 17.500000
## 1190 28.30976 30.16407 22.021335 13.581984 25.90294 28.37664 14.70000 18.900000
## 1211 24.66802 26.78273 13.342864  8.919463 19.84097 25.19713 37.70000  7.470000
## 1232 25.39097 26.69613 20.083060  7.158283 23.30618 28.17372 25.30000  7.830000
## 1253 24.82501 28.62742 22.004654  8.871887 23.74250 28.31769 28.30000 16.700000
## 1274 24.39808 26.19344  9.526062 11.755902 21.52548 20.79691 63.39871  4.953034
## 1275 27.33835 28.89657 18.031914  9.895583 25.98677 27.80121 45.40000 16.100000
## 1296 25.71248 29.79103 15.178442 10.393341 22.46358 27.50077 47.53241  7.994329
## 1297 22.21427 25.55363  9.661429 12.013956 18.22035 19.84642 67.80764  4.114265
## 1298 22.32535 24.21456  8.805372 11.222371 20.81727 19.51511 66.45499  4.407345
## 1299 28.81700 27.39392 16.855267  8.065261 20.60755 23.72752 21.90510 13.203145
## 1300 24.59491 26.21215  9.441200 11.032251 20.47256 20.23849 64.71646  4.725783
## 1301 22.79706 23.15837  9.722345 12.451737 20.65242 17.12110 64.71388  4.796189
## 1302 25.96324 26.79236 13.220426 14.919748 20.57782 22.50920 71.22315  3.236726
## 1303 26.55833 27.50508 10.058471 12.308402 22.04861 23.31357 67.81498  3.939915
## 1304 27.12947 24.06304 10.004453 11.728596 24.10621 21.72844 58.17726  5.943724
## 1305 24.81094 23.62678  9.365788 11.073955 17.51909 19.97281 59.27656  5.856331
## 1306 22.33892 23.77440  9.192797 11.689073 18.09591 20.75239 61.72486  5.325074
## 1307 23.72726 23.93945 15.923781  7.757090 27.17214 25.43076 34.25674 10.743218
## 1308 25.57028 25.88464 11.281775 12.009186 21.10553 22.98131 66.97481  4.216435
## 1309 24.28717 26.03423 10.982572 12.330284 20.82249 21.80276 67.91418  4.012897
## 1310 23.38032 25.96738 10.944685 11.589537 23.26258 20.96380 67.89808  4.021087
## 1311 22.97322 27.10505  8.885230 11.076750 16.22269 18.16059 62.27610  5.308453
## 1312 23.58751 24.29967 10.008384 10.705349 22.69601 20.23189 57.52131  6.161875
## 1313 26.18841 24.98712 10.995071 10.401308 20.49431 21.47300 48.32911  8.045088
## 1314 19.15281 19.06725  7.547668 11.758377 17.68614 16.26408 63.75547  5.158546
## 1315 23.92244 25.49845  9.999622  9.756697 21.55859 21.56077 55.90000  6.210000
## 1334 25.46251 26.64396  9.906633 10.453336 22.37117 22.84888 61.40000  4.100000
## 1353 22.09169 28.07379  9.665312  9.605007 20.07661 20.12716 47.20000  5.660000
## 1372 26.34456 23.49281  9.661908  9.225406 21.56845 22.03168 48.20000 12.400000
## 1391 24.33736 23.94627 12.576263 12.867410 20.08692 20.32090 65.29534  4.632881
## 1392 24.56416 24.18033 11.724569 10.427714 27.52816 26.40631 50.10000  7.080000
## 1411 26.30723 23.92448  7.985537  9.284697 19.19948 18.33022 41.90000 11.900000
## 1430 21.91255 28.09646  5.846103 10.089456 18.04099 19.42994 46.80000 10.900000
## 1449 26.67078 22.17432  8.544338  9.708748 19.40001 18.85483 41.60000 12.900000
## 1468 23.22055 25.97794 11.387807 11.285548 22.13123 21.26313 44.30000  6.690000
## 1487 24.99690 27.23902 10.488427 11.445105 22.62902 25.72357 32.60000  3.810000
## 1506 21.03695 22.26847 10.541307  9.678177 16.57747 17.42194 43.40000  5.480000
## 1525 23.67079 24.50357  8.477857 10.166476 17.26136 17.46136 46.30000  8.690000
## 1544 19.99873 23.04488 10.740533 11.861010 20.43164 17.80555 66.37226  4.474737
## 1545 29.54948 22.51975 10.484425 12.108359 20.85134 16.34527 31.60000 12.300000
## 1564 23.17187 25.18772 11.220518  9.548208 22.43535 22.25967 49.20000  9.760000
## 1583 21.21622 24.28532 10.960531  8.968065 23.16068 23.48999 53.40000 10.400000
## 1602 22.99591 24.24964 10.019037  8.531568 21.37614 20.82249 60.00000  5.260000
## 1621 25.42788 24.80184  8.590075  8.871211 17.57769 19.73392 47.60000  5.920000
## 1640 23.75737 20.88617  6.927890 13.408973 17.53509 16.57800 63.30710  5.162081
## 1641 26.10599 28.46406 10.098975 10.393635 23.04097 22.15808 50.60000  5.740000
## 1660 24.42321 27.33021 10.388094  8.474758 19.93831 21.28205 53.00000  5.560000
## 1679 23.58885 25.53393 10.990366  9.615567 19.35312 22.38159 43.09000  6.120000
## 1700 23.96642 25.84580 11.220712 10.047665 19.78065 22.52779 43.27000  7.155000
## 1721 19.91930 21.95239  5.077671 10.093173 14.29073 16.82870 42.08500  9.840000
## 1742 22.72406 24.52266  8.480967 11.148774 17.88449 19.81789 54.71000  7.000000
## 1763 22.78997 24.69268  9.292456 10.242361 18.14960 20.71082 49.70000  6.950000
##      F_Div_Treg  F_Treg17       F_Th1    F_Div_Th1    F_Th17 F_Div_Th17
## 1    16.2050000 13.520000  6.78000000  71.20000000 0.8900000 46.8750000
## 22   21.3650000 11.565000 10.92000000  75.11500000 1.0750000 42.3900000
## 43   12.4550000  9.505000  2.96500000  19.84000000 1.6300000 30.0550000
## 64   23.7600000 12.780000  9.25000000  81.21000000 1.7050000 78.3050000
## 85   13.4650000 14.400000  2.54500000  27.85000000 1.0600000 27.4450000
## 106  13.3550000  7.035000  2.90000000  25.52000000 0.6950000 32.1950000
## 127  24.7950000 13.950000  6.87000000  76.51500000 1.1100000 65.7350000
## 148  17.1150000  8.645000  9.58500000  51.87000000 1.0900000 40.6000000
## 169  21.0000000 14.540000  7.02000000  67.36000000 1.6150000 65.0550000
## 190  13.0900000  6.825000  7.71000000  79.02000000 1.1850000 55.8350000
## 211  14.1100000 14.350000  1.73000000  14.31000000 0.9250000 33.0750000
## 232  20.5150000  9.260000  9.10000000  64.37000000 0.8050000 49.9100000
## 253   7.8500000  9.015000  2.50500000  19.19000000 0.9450000 28.8150000
## 274  16.7750000 13.315000  4.84000000  54.63500000 0.9700000 35.2750000
## 295  12.7100000  9.660000  1.87500000  29.57500000 0.5350000 21.1550000
## 316  12.1100000  7.535000  1.45500000  21.43500000 0.5500000 22.9200000
## 337  24.1100000  8.970000 11.54000000  90.78000000 4.0500000 67.7800000
## 358  13.0050000  9.130000  1.83500000  22.30000000 1.0750000 30.8900000
## 379  16.5750000 15.280000  4.44500000  48.20500000 0.7950000 28.3550000
## 400  15.7950000  4.280000  3.71000000  75.72000000 0.7650000 46.7200000
## 421  41.6050000 11.280000  6.79500000  59.59000000 1.2250000 36.9600000
## 442   8.2600000  4.370000  1.53500000  22.66500000 0.5800000 19.2300000
## 463  38.2100000  8.875000  5.92500000  65.70000000 1.9350000 41.4550000
## 484  23.5250000  7.885000  3.54000000  53.20000000 1.3600000 24.0450000
## 505  10.5500000  4.220000  1.42500000  22.07500000 1.0700000 31.6650000
## 526  36.5850000 17.105000  3.37000000  72.07500000 0.6200000 38.0400000
## 547  16.8350000 13.005000  1.73500000  11.91000000 1.1200000 50.0700000
## 568  13.0000000 12.720000  1.85500000  13.03500000 1.4400000 19.1200000
## 589  30.5800000 17.135000  5.61500000  41.68000000 0.9750000 22.3550000
## 610  43.8550000 12.800000  5.39500000  56.13000000 0.8750000 30.4600000
## 631   8.2250000 10.045000  1.78000000  31.14500000 1.1100000 63.2350000
## 652  22.1500000 12.340000  1.83500000  34.18000000 1.0100000 12.9050000
## 673  31.7950000 18.210000  2.74000000  21.99000000 0.7300000 27.2750000
## 694  19.2450000  7.590000  3.11000000  35.55500000 1.4350000 39.9950000
## 715  31.5950000  9.750000  3.05500000  29.22000000 0.8400000 30.1700000
## 736  17.7300000 12.165000  1.51000000  28.17000000 0.6600000  9.7000000
## 757  19.5500000  6.445000  1.30500000  27.14000000 0.4850000 19.2000000
## 778  52.6200000 14.605000  7.42500000  79.50500000 1.7300000 69.7000000
## 799  36.4750000 18.505000  5.28000000  48.67000000 1.9400000 24.2200000
## 820  26.9400000  8.940000  3.02000000  22.30500000 1.4000000 21.8050000
## 841  19.4950000  6.415000  1.37500000  27.52000000 0.8300000 28.2850000
## 862  36.7750000  9.390000  4.13000000  60.85500000 0.6800000 27.7100000
## 883  34.9950000  6.330000  2.81000000  76.26500000 0.6350000 46.6900000
## 904  12.9050000  5.325000  1.33000000  22.60000000 0.6300000 23.0550000
## 925  35.2350000  8.230000  5.48000000  76.18500000 1.4300000 59.0400000
## 946  13.7200000  5.700000  1.23500000  29.35000000 0.5900000 20.9100000
## 967  22.7750000 17.040000  1.83500000  15.25500000 1.0800000 12.2200000
## 988  25.7925153 10.277050  4.50541369  54.56648742 1.0270539 36.9829471
## 989  33.9800000  5.645000  4.17000000  58.70500000 0.5200000 39.0650000
## 1010  9.2234698  9.239743 -0.45538458   6.58905175 0.6842821  9.6370752
## 1011 11.2134851  9.582648  0.57373710  12.29371706 0.8989550 14.4335789
## 1012  9.0943922  8.771985 -1.40636741   6.33664696 0.3162953  6.2401996
## 1013 10.1740304  9.538581  0.30421796   9.27831927 0.8941468 12.8635495
## 1014  5.6354108  8.511990 -2.52821773  -3.66773273 0.2102112  0.2305219
## 1015 16.7498936  9.209124  0.80207653  28.51508990 0.4411832 18.5831541
## 1016  9.8579970  9.033281 -0.75402050   8.49138514 0.5017586  8.9791606
## 1017 11.2215137  9.280541 -0.02445842  12.39685272 0.6586300 12.3508776
## 1018 17.4979083 10.274588  3.04775290  30.41213001 1.2661917 26.8730236
## 1019 13.5711782  9.017313 -0.13536101  19.30888204 0.3811395 13.3867569
## 1020  6.5397041  8.857508 -1.68407328  -1.12552495 0.4585178  3.7240095
## 1021  8.4559290  9.070628 -0.92546578   4.39851582 0.5721917  7.5317957
## 1022 26.7000000  7.630000  8.13000000  60.60000000 3.7000000 62.1000000
## 1043 34.5000000 11.500000 13.10000000  63.50000000 3.1600000 64.2000000
## 1064 25.6000000  9.050000  4.78000000  54.90000000 2.1700000 42.7000000
## 1085  8.0700000  3.230000  4.83000000  15.80000000 1.5300000 13.3000000
## 1106 24.9000000  8.360000 11.60000000  54.60000000 3.0300000 43.8000000
## 1127 30.6000000 12.100000  6.87000000  63.10000000 3.7900000 65.8000000
## 1148 23.2000000  9.410000  9.08000000  49.70000000 2.0600000 52.4000000
## 1169 13.6000000 17.500000  5.73000000  19.40000000 2.3500000 12.4000000
## 1190 30.0000000 20.000000 12.40000000  58.70000000 3.0300000 58.0000000
## 1211 21.6000000 10.500000  2.70000000  27.50000000 1.5100000 54.3000000
## 1232 41.2000000  8.040000  6.85000000  83.70000000 1.1900000 73.9000000
## 1253 45.1000000  5.680000  7.26000000  70.20000000 1.4500000 47.7000000
## 1274 10.8439750  9.143735 -0.36210720  11.33352560 0.5608805 10.9439328
## 1275  6.4800000  3.430000  3.26000000  12.60000000 1.0000000  9.2000000
## 1296 16.4161860 10.534150  3.37345277  27.19348619 1.5039131 27.3545739
## 1297  9.5128330  8.758138 -1.36056058   7.55885808 0.2931285  6.6534531
## 1298 11.1450308  8.880784 -0.83126606  12.27965884 0.3431563  9.4889901
## 1299 35.5670557 12.815999 11.25653524  82.36098531 2.7607086 66.4620329
## 1300 11.2486247  9.031337 -0.51431457  12.54159262 0.4597932 10.6578112
## 1301 13.6429182  9.040063 -0.07764333  19.51179282 0.3971343 13.6316223
## 1302  0.6843582  8.431735 -3.55466517 -18.06467458 0.2903394 -6.3497864
## 1303  3.5896149  8.736471 -2.44099856  -9.68462684 0.4480752 -0.7039911
## 1304 12.3295266  9.600071  0.80378829  15.53918000 0.8803622 15.9122536
## 1305 16.1653487  9.518729  1.31417769  26.73108238 0.7042240 20.0162810
## 1306 13.1843463  9.296644  0.35128419  18.10863336 0.6143744 14.8507789
## 1307 28.0683141 11.722360  7.77257039  60.81236170 2.1095281 49.7627371
## 1308  7.8378952  8.824135 -1.52293404   2.66379168 0.3942624  5.0725031
## 1309  6.7042510  8.738962 -1.89053573  -0.61504663 0.3595236  3.1031493
## 1310  6.8846005  8.740993 -1.85491615  -0.09038093 0.3558960  3.3366690
## 1311 16.0390445  9.259162  0.77688453  26.43179232 0.5016110 18.0647658
## 1312 15.7237101  9.668789  1.53465643  25.40535608 0.8363176 20.5182543
## 1313 23.1021534 10.489068  4.45500524  46.67582097 1.2737491 35.1778112
## 1314 20.0953875  9.145769  1.26228600  38.27434221 0.2935918 22.2151335
## 1315 26.4000000 17.500000  5.01000000  44.10000000 1.4700000 29.7000000
## 1334 21.4000000 15.500000  1.46000000  33.80000000 0.9900000 17.0000000
## 1353 30.2000000 18.500000  6.80000000  63.40000000 1.5200000 47.9000000
## 1372 17.9000000  7.520000  3.99000000  52.90000000 2.2000000 36.6000000
## 1391 11.6635824  8.982804 -0.53796184  13.76281785 0.4091612 10.8265767
## 1392 22.2000000 14.200000  4.95000000  59.50000000 1.0700000 23.7000000
## 1411 22.2000000  5.660000  4.86000000  55.80000000 1.2600000 26.5000000
## 1430 24.1000000  5.860000  4.20000000  66.30000000 1.5200000 48.0000000
## 1449 23.0000000  6.090000  3.80000000  56.50000000 2.1600000 35.9000000
## 1468 40.9000000 11.000000  6.35000000  71.90000000 1.4700000 56.2000000
## 1487 43.2000000 13.500000  4.17000000  53.80000000 4.8100000 11.1000000
## 1506 24.9000000 13.100000  5.66000000  62.70000000 1.3200000 36.1000000
## 1525 32.7000000 13.300000  9.29000000  51.80000000 1.2800000 27.1000000
## 1544 12.9382851  8.894299 -0.49035912  17.49828768 0.3017737 11.7646302
## 1545 26.1000000  4.030000  6.46000000  81.70000000 1.2300000 46.3000000
## 1564 15.3000000  6.320000  2.26000000  29.60000000 1.3900000 26.3000000
## 1583 14.3000000  4.680000  1.58000000  43.20000000 1.5200000 37.1000000
## 1602 16.0000000  9.450000  3.04000000  26.10000000 0.7800000 15.1000000
## 1621 24.3000000 13.400000  5.75000000  46.10000000 1.1200000 31.9000000
## 1640 17.4315703  9.175042  0.85382679  30.50922132 0.3942837 19.1765585
## 1641 18.5000000  7.520000  2.02000000  20.70000000 1.3100000 15.3000000
## 1660 21.9000000 12.800000  4.38000000  41.90000000 1.4100000 25.8000000
## 1679 21.8850000 25.480000  7.62000000  60.78000000 1.4150000 45.3250000
## 1700 16.3650000 10.450000  5.48500000  65.42500000 1.1700000 29.2700000
## 1721 37.8500000  8.350000  7.57000000  71.14000000 1.0050000 41.1050000
## 1742 17.5700000  9.180000  1.31500000  26.47500000 0.9250000 25.8150000
## 1763 30.0000000 19.500000  5.04000000  62.20000000 1.4100000 42.0000000
##          F_CD8  F_Act_CD8 F_Div_Act_CD8  F_IFNy_CD4 F_IFNy_CD8
## 1    14.390000 11.5000000   49.52000000  4.91500000  21.740000
## 22   13.840000 13.2050000   59.09000000  9.08500000  27.535000
## 43   10.020000 10.9150000   11.53500000  3.04500000  41.360000
## 64   25.305000 11.1050000   55.93500000  9.08500000  38.165000
## 85   17.550000  9.8150000   12.83000000  2.00500000  19.390000
## 106   7.490000  5.3950000   21.31000000  2.79500000  19.230000
## 127   9.065000  8.9000000   55.69000000  8.45500000  34.310000
## 148  13.995000  9.2000000   55.97000000  8.75500000  28.690000
## 169   8.840000  8.3750000   45.89500000 12.91000000  46.265000
## 190  26.505000 18.2600000   38.45000000  4.59000000  27.800000
## 211  13.900000  3.7850000    8.98500000  1.69000000  13.755000
## 232  31.115000 13.4600000   38.51500000  9.60000000  30.505000
## 253  18.080000  3.4550000    8.71000000  1.95000000  13.490000
## 274  19.235000  4.9300000   44.24000000  4.35500000  23.725000
## 295  17.080000  4.7550000   15.41000000  1.81000000  11.825000
## 316  28.360000  5.6400000    6.33500000  1.65000000  16.100000
## 337  24.175000 20.5000000   29.36500000  3.24000000  27.110000
## 358  11.410000  4.4550000   12.67500000  2.58000000  22.560000
## 379  17.805000  7.6300000   39.93000000  4.84500000  26.830000
## 400  33.620000 13.3850000   33.36500000  1.74000000  16.375000
## 421  12.140000 10.9100000   47.47500000  2.62000000  16.955000
## 442  26.665000  4.7400000    7.56000000  1.76000000  14.625000
## 463  35.575000 19.1750000   31.74500000  3.05500000  19.105000
## 484  17.865000 11.1250000   29.02000000  2.90000000  23.535000
## 505  27.880000  6.6150000   10.91000000  1.11000000  20.565000
## 526  27.135000  6.9450000   43.53500000  2.01500000  11.530000
## 547  18.020000  2.3650000    6.81500000  1.13000000   8.335000
## 568  14.640000  4.7650000    7.74000000  1.82500000  15.605000
## 589   7.365000 16.4150000   45.83000000  5.47500000  31.365000
## 610  13.720000  7.8450000   51.61000000  5.17000000  21.410000
## 631  20.855000  3.8550000   10.13000000  1.36000000   8.335000
## 652  12.335000  3.9850000   23.61000000  1.22500000  10.585000
## 673  18.260000  2.8100000   15.01500000  2.01000000   8.880000
## 694  26.645000 17.7350000   27.74500000  3.21500000  34.270000
## 715   7.725000  6.5000000   18.93000000  3.60500000  22.265000
## 736  21.500000  3.3250000   12.07500000  0.41000000   3.605000
## 757  27.155000  3.9000000    6.50000000  1.02000000   7.420000
## 778  21.090000 21.6050000   36.21000000  2.31500000  14.775000
## 799  18.575000  8.1350000   46.22000000  2.31000000  17.460000
## 820  10.375000  6.7200000   17.13500000  2.78000000  18.350000
## 841  29.465000  4.2200000   16.69500000  0.83000000   9.825000
## 862  24.340000  7.4100000   46.67000000  1.32500000   4.500000
## 883  28.860000  7.4600000   43.70000000  1.45500000   7.310000
## 904  33.330000  8.9100000    6.91500000  0.40000000   5.010000
## 925  34.585000 15.0350000   35.20500000  1.54000000   9.265000
## 946  32.530000  7.5800000    9.96000000  0.43000000   4.315000
## 967  14.995000  5.1600000    6.56500000  1.07000000   9.730000
## 988  23.845654 11.7349682   34.78714928  2.81001790  17.185952
## 989  28.025000 12.9850000   49.65000000  2.52000000  14.905000
## 1010 19.464310  1.5948087    2.14725525 -0.35490981   9.380939
## 1011 17.893490  3.0858048    5.62848650  0.44754326  12.142309
## 1012 23.845528  0.9407591    2.81722605 -1.26879078   5.479772
## 1013 17.416839  2.4759454    3.53859192  0.28963135  11.828174
## 1014 23.348170 -1.2304664   -3.90938096 -2.01354790   3.487428
## 1015 26.274581  5.5731717   17.98276054  0.11201408   8.733962
## 1016 21.996798  1.6751572    3.89222262 -0.70994259   7.623081
## 1017 20.802276  2.7121546    6.25274092 -0.13630794   9.617946
## 1018 16.688055  7.3052620   17.40819095  2.22540068  17.596489
## 1019 25.364950  3.6368139   11.70644584 -0.48125336   7.296685
## 1020 20.812274 -0.3156260   -2.70886647 -1.28194259   6.327048
## 1021 20.424202  0.9736284    0.87866642 -0.73571271   8.007961
## 1022  6.580000 11.4000000   24.40000000  5.82000000  38.300000
## 1043  9.980000 16.2000000   22.80000000  8.40000000  37.700000
## 1064  8.930000 12.4000000   30.60000000  2.85000000  23.700000
## 1085 15.400000 13.7000000    6.07000000  3.05000000  21.500000
## 1106  7.770000 19.5000000   26.50000000  9.34000000  36.900000
## 1127  5.870000 12.8000000   16.60000000  3.91000000  28.900000
## 1148  6.140000 20.5000000   22.50000000  9.13000000  50.100000
## 1169 14.700000  3.3700000   11.80000000  6.59000000   7.210000
## 1190  7.300000 10.2000000   23.50000000  9.70000000  21.500000
## 1211 10.500000  7.5200000    7.41000000  2.72000000  20.300000
## 1232  7.560000 26.5000000   42.50000000  3.11000000  22.000000
## 1253  5.790000 25.9000000   62.70000000  2.70000000  14.500000
## 1274 21.789482  2.3395272    5.73656000 -0.42734645   8.494623
## 1275 11.500000  7.3700000    8.52000000  1.11000000  20.100000
## 1296 13.258358  7.0526841   14.61922374  2.65189025  19.821210
## 1297 24.340803  1.1467486    3.72203830 -1.26632424   5.342343
## 1298 24.575859  2.1712423    6.89621519 -0.91496768   6.282157
## 1299  7.920738 20.1276186   50.16780110  8.40481967  37.889540
## 1300 23.219438  2.4148690    6.81062062 -0.61648781   7.534563
## 1301 25.208540  3.7035599   11.81105732 -0.43222939   7.483021
## 1302 19.832564 -3.9731201  -14.12447452 -2.51491426   3.074261
## 1303 19.420767 -2.0414326   -8.64832685 -1.72231667   5.469174
## 1304 18.692375  3.7032046    7.93246456  0.55926688  12.229864
## 1305 22.794646  5.6485288   16.13516358  0.67006622  11.350928
## 1306 22.346976  3.7798136   10.33408651  0.03205768   9.650455
## 1307 11.933296 14.7575857   36.65078905  5.76500388  29.142490
## 1308 22.256764  0.3354380    0.07899296 -1.25574638   5.980768
## 1309 22.093408 -0.3761121   -2.12565440 -1.49976164   5.328105
## 1310 22.230037 -0.2773227   -1.75176086 -1.48322408   5.335703
## 1311 25.178521  5.2564058   16.39234966  0.15911567   9.188945
## 1312 20.970906  5.6005627   14.90785586  0.92947805  12.627560
## 1313 19.479906 10.5644231   28.72237341  3.03208182  19.097084
## 1314 29.779567  7.2793216   25.12165996  0.22333100   8.030783
## 1315 14.400000 14.4000000   29.10000000  2.87000000  23.500000
## 1334 16.400000  4.3700000   11.60000000  0.34000000  16.200000
## 1353 15.600000 11.2000000   38.90000000  2.84000000  18.200000
## 1372 26.100000 14.0000000   38.10000000  1.67000000  21.100000
## 1391 24.044876  2.5756065    7.77787573 -0.68136486   7.107525
## 1392 13.800000 11.2000000   43.00000000  2.24000000  35.600000
## 1411 27.900000 13.3000000   54.10000000  1.85000000  12.200000
## 1430 28.400000 13.9000000   41.80000000  2.60000000  23.500000
## 1449 25.200000 10.7000000   44.30000000  2.05000000  15.100000
## 1468 13.900000 20.9000000   52.20000000  2.92000000  31.400000
## 1487 14.400000 12.1000000   28.80000000  1.96000000  25.100000
## 1506 14.500000 18.6000000   40.20000000  1.89000000  16.700000
## 1525 13.900000 19.4000000   32.90000000  6.73000000  28.300000
## 1544 25.998592  3.1451651   10.62735869 -0.76346133   6.301866
## 1545 23.800000 20.9000000   48.60000000  2.36000000   8.070000
## 1564 23.300000 14.1000000   15.50000000  1.54000000  18.200000
## 1583 24.500000 13.4000000   16.20000000  1.32000000  16.900000
## 1602 17.500000  8.3800000    7.74000000  1.70000000  14.500000
## 1621 19.100000 11.5000000   27.00000000  3.97000000  32.000000
## 1640 27.192125  5.8943294   19.47995061  0.09373862   8.413797
## 1641 11.700000  7.1100000    7.55000000  0.75000000  24.400000
## 1660 15.000000  7.4700000   33.40000000  2.13000000  22.100000
## 1679 16.055000  7.8150000   60.25500000  8.38000000  29.545000
## 1700 10.565000 11.4300000   47.35500000  3.41500000  26.765000
## 1721 30.975000 27.8050000   38.40000000  3.78500000  12.825000
## 1742 26.945000  9.6650000   10.63000000  0.66500000   7.805000
## 1763 14.800000 10.9000000   41.40000000  1.06000000  10.300000

Caution: When imputing data, the percentages of inertia associated with the first dimensions will be overestimated.

Another problem: the imputed data are, when the pca is performed considered like real observations. But they are estimations!!

Visualizing uncertainty due to issing data:

–> mulrimple imputation: generate several plausible values for each missing data point

We here visualize the variability, that is uncertainty on the plane defined by two pca axes.

## $PlotIndProc

## 
## $PlotDim

## 
## $PlotIndSupp

## 
## $PlotVar

Individuals lying on the axis have no missing data, but individuals that far away have many missing data. big ellipse = big uncertainty tight elipse (line) = low uncertainty

Variable representation: Poins tight together )look like one) - have no missing variables –> low uncertainty Points spread – > higher variability – > higher uncertainty

High uncertainty–> we should interpret the result with care

The individuals with many missing data values make the axes move, and thus the positions of all individuals

Therefore in the last plots every individual is getting an eclipse as they are as well influenced by the missing data of the others.

THe plot with the dimensions shows the projections of the pca dimensions of each imputed table on the pca plane obtained using the original imputed data table

As all of the arrows are close to either the first or second axes, this means that the axes are stable with respect to the set of imputed tables –> we don’t have evidence of instability here.

The total contribution to PC1 and PC2 is obtained with the following R code:

## 
## Call:
## lm(formula = max_WL ~ pc1 + pc2 + challenge_infection, data = i)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -14.3918  -2.9882   0.1399   3.3999  14.5286 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             91.4040     0.7929 115.273  < 2e-16 ***
## pc1                      0.3241     0.1528   2.121    0.036 *  
## pc2                     -0.3118     0.1999  -1.559    0.122    
## challenge_infectionE88  -6.3572     1.4003  -4.540 1.38e-05 ***
## challenge_infectionUNI   5.3304     1.2172   4.379 2.62e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.288 on 116 degrees of freedom
## Multiple R-squared:  0.3672, Adjusted R-squared:  0.3453 
## F-statistic: 16.83 on 4 and 116 DF,  p-value: 6.65e-11

## [1] 753.3354

## 
## Call:
## lm(formula = max_WL ~ pc1 + pc2, data = i)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -18.564  -3.091   1.954   5.027   8.267 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 92.36267    0.59853 154.315   <2e-16 ***
## pc1          0.02925    0.18460   0.158    0.874    
## pc2          0.10769    0.21803   0.494    0.622    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.584 on 118 degrees of freedom
## Multiple R-squared:  0.002275,   Adjusted R-squared:  -0.01464 
## F-statistic: 0.1345 on 2 and 118 DF,  p-value: 0.8743

##                    df      AIC
## weight_lm           6 753.3354
## weight_lm_exp_only  4 804.4239

heatmap?

Gene_expression

Fay

09/05/2022

Gene expression - laboratory infections

Gene correlation

Correlations between genes:

Correlations between genes, only including significant values

Handling missing data in a pca:

repeating the heatmap on the now imputed data

Note: Using an external vector in selections is ambiguous.

ℹ Use all_of(CellCount.cols) instead of CellCount.cols to silence this message.

ℹ See https://tidyselect.r-lib.org/reference/faq-external-vector.html.

This message is displayed once per session.

Adding missing grouping variables: infection

stat_bin() using bins = 30. Pick better value with binwidth.

Warning: Removed 66 rows containing non-finite values (stat_bin).

stat_bin() using bins = 30. Pick better value with binwidth.

Warning: Removed 46 rows containing non-finite values (stat_bin).

EH_ID primary_infection challenge_infection infection_history

Length:85 Length:85 Length:85 Length:85

Class :character Class :character Class :character Class :character

Mode :character Mode :character Mode :character Mode :character

mouse_strain hybrid_status Parasite_primary Parasite_challenge

Length:85 Length:85 Length:85 Length:85

Class :character Class :character Class :character Class :character

Mode :character Mode :character Mode :character Mode :character

max_WL max_OOC Position CD4

Min. : 73.45 Min. : 0 Length:85 Min. :14.60

1st Qu.: 89.78 1st Qu.: 0 Class :character 1st Qu.:42.86

Median : 94.58 Median : 72500 Mode :character Median :48.49

Mean : 92.96 Mean : 260500 Mean :46.52

3rd Qu.: 97.72 3rd Qu.: 317500 3rd Qu.:53.40

Max. :100.00 Max. :1447500 Max. :68.01

Treg Div_Treg Treg17 Th1

Min. : 3.245 Min. : 6.48 Min. : 3.230 Min. : 1.235

1st Qu.: 5.650 1st Qu.:16.00 1st Qu.: 7.520 1st Qu.: 2.505

Median : 7.005 Median :22.15 Median : 9.505 Median : 4.380

Mean : 8.382 Mean :23.41 Mean :10.646 Mean : 4.822

3rd Qu.: 9.840 3rd Qu.:30.20 3rd Qu.:13.400 3rd Qu.: 6.795

Max. :27.230 Max. :52.62 Max. :25.480 Max. :13.100

Div_Th1 Th17 Div_Th17 CD8

Min. :11.91 Min. :0.485 Min. : 9.20 Min. : 5.79

1st Qu.:27.50 1st Qu.:0.925 1st Qu.:24.22 1st Qu.:12.14

Median :51.87 Median :1.185 Median :33.08 Median :16.40

Mean :47.54 Mean :1.390 Mean :36.65 Mean :18.24

3rd Qu.:63.50 3rd Qu.:1.520 3rd Qu.:46.88 3rd Qu.:25.30

Max. :90.78 Max. :4.810 Max. :78.31 Max. :35.58

Act_CD8 Div_Act_CD8 IFNy_CD4 IFNy_CD8

Min. : 2.365 Min. : 6.07 Min. : 0.340 Min. : 3.605

1st Qu.: 6.720 1st Qu.:12.07 1st Qu.: 1.670 1st Qu.:13.490

Median :10.700 Median :29.02 Median : 2.580 Median :19.390

Mean :10.927 Mean :29.06 Mean : 3.358 Mean :20.395

3rd Qu.:13.700 3rd Qu.:43.53 3rd Qu.: 3.910 3rd Qu.:26.830

Max. :27.805 Max. :62.70 Max. :12.910 Max. :50.100

Treg_prop IL17A_CD4 delta

Min. :72.66 Min. :0.1650 Min. :-12.690

1st Qu.:91.25 1st Qu.:0.3850 1st Qu.: -8.791

Median :93.28 Median :0.5075 Median : -6.840

Mean :92.62 Mean :0.6732 Mean : -5.561

3rd Qu.:94.68 3rd Qu.:0.7825 3rd Qu.: -4.750

Max. :96.64 Max. :2.3400 Max. : 11.610

NA’s :33 NA’s :33 NA’s :3

Warning in par(usr): argument 1 does not name a graphical parameter

Warning in par(usr): argument 1 does not name a graphical parameter

Warning in par(usr): argument 1 does not name a graphical parameter

Warning in par(usr): argument 1 does not name a graphical parameter

Warning in par(usr): argument 1 does not name a graphical parameter

Warning in par(usr): argument 1 does not name a graphical parameter

Warning in par(usr): argument 1 does not name a graphical parameter

Warning in par(usr): argument 1 does not name a graphical parameter

Warning in par(usr): argument 1 does not name a graphical parameter

Warning in par(usr): argument 1 does not name a graphical parameter

Warning in par(usr): argument 1 does not name a graphical parameter

Warning in par(usr): argument 1 does not name a graphical parameter

Warning in par(usr): argument 1 does not name a graphical parameter

Warning in par(usr): argument 1 does not name a graphical parameter

Warning in par(usr): argument 1 does not name a graphical parameter

Warning in par(usr): argument 1 does not name a graphical parameter

Warning in par(usr): argument 1 does not name a graphical parameter

Warning in par(usr): argument 1 does not name a graphical parameter

ℹ Use `all_of(CellCount.cols)` instead of `CellCount.cols` to silence this message.

Adding missing grouping variables: `infection`

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.